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Funded PhDs

PhD titleResearch groupSupervisorClosing date
ICASE PhD studentship: Music Data Science for Music Recommendation and Discovery Centre for Digital Music (C4DM) Professor Mark Sandler 31/03/2018
PhD Studentship in passive and active antennas and quasi-optical devices for mm-and THz applications Antennas & Electromagnetics   11/02/2018
PhD Studentship in Understanding Audio-visual Interactions through Multi-view Mapping In one of the research groups   31/01/2018
PhD Studentships in Electronic Engineering and Computer Science In one of the research groups   31/01/2018
Closed
PhD Studentship in Computer Vision & Robotics (2018) Computer Vision   31/01/2018
Closed
PhD Studentship in passive and active antennas and quasi-optical devices for mm-and THz applications Antennas & Electromagnetics  

15/10/2017
Closed

PhD Studentship in Dynamic Adaptive Automated Software Engineering Theoretical Computer Science (Theory)   31/08/2017 Closed
Data analytics for video distribution applications Multimedia & Vision (MMV)   04/08/2017 Closed
Two PhD Studentships in Music Data Science at the Centre for Digital Music Centre for Digital Music (C4DM)   30/06/2017 Closed
Studentship in Computer based histopathological image analysis for cancer grading and progression Multimedia & Vision (MMV)   23/06/2017 Closed
PhD Studentship in the measurement of user quality of experience in broadband networks Networks   14/06/2017 Closed
PhD Studentship in deep learning for mobile camera networks Centre for Intelligent Sensing   15/05/2017 Closed
PhD Studentships in Electronic Engineering and Computer Science In one of the Research Groups   28/04/2017 Closed
PhD Studentship in Bayesian Artificial Intelligence for Decision Making Under Uncertainty Risk and Information Management (RIM)   14/04/2017 Closed
Two PhD Studentships in Medical Decision Support using Bayesian Networks Risk and Information Management (RIM)   24/03/2017 Closed
PhD Studentship in Intelligent Robotics Robotics   15/02/2017 Closed
PhD Studentship in Human-Robot Interaction, Haptics and/or VR Robotics   15/02/2017 Closed

 

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ICASE PhD studentship: Music Data Science for Music Recommendation and Discovery


Application closing date: 31/03/2018 
Start date: 30/09/2018 
Research group: Centre for Digital Music (C4DM)

Duration: 4 years
Funding available

 

Supervised by Professor Mark Sandler, Centre for Digital Music; co-supervised by Dr Enzo Nicosia, School of Mathematical Sciences

This well-funded ICASE PhD (funded for four years with full UK fees plus tax-free stipend of nearly £20k) will investigate music discovery and recommendation for professional users, such as radio DJs and documentary producers, as well as for music consumers. The project is part of the on-going relationship between Queen Mary and the BBC including the Audio Research Partnership1 and the Data Science Research Partnership2.

The successful candidate will study in the world-renowned Centre for Digital Music at Queen Mary and spend at least 1 month per year in BBC R&D Labs. The project is associated with the EPSRC-funded Programme Grant, Fusing Audio and Semantic Metadata for Intelligent Music Production and Consumption (see semanticaudio.ac.uk).

The research builds on a previous collaboration3 and extends it in several ways. The project will include many new musical audio features such as key, meter and instrumentation that are already under development in the Centre for Digital Music, and couple these so-called Content Derived Metadata (CDM) with other BBC metadata such as artist, genre and mood. The technical approach to be adopted will use Linked Data and Graph Theory, and enable CDM to be integrated into well-established collaborative filtering approaches to recommendation.

The project will include both scientific and technological development, as well as user studies with stakeholders from the BBC. The successful applicant should have a strong interest in music and sound, excellent programming skills and be capable of working with advanced mathematical concepts from Graph Theory and Linear Algebra. Understanding of DSP and Machine Learning is advantageous.

Candidates must have a first-class honours degree (or exceptionally, a high upper second) or equivalent, or a good MSc Degree in Computer Science, Electronic Engineering, Sound & Music Computing or equivalent. Experience in research and a track record of publications is very advantageous.

To apply, please follow the on-line process at (www.qmul.ac.uk/postgraduate/applyresearchdegrees/); click on the list of Research Degree Subjects, select ‘Electronic Engineering’ in the ‘A-Z list of research opportunities’, and follow the instructions on the right-hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement should answer the following questions: (i) Why are you interested in the topic? (ii) What relevant experience do you have? (iii) How you would begin your approach to the research? The statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work (e.g. excerpt of final year dissertation or published academic paper). More details can be found at: http://www.eecs.qmul.ac.uk/phd/how-to-apply

The closing date for applications is 31 March 2018, and interviews are expected to take place around the middle of April, with a start date as soon as possible and no later than 30 September 2018. Enquiries may be addressed to mark.sandler@qmul.ac.uk.

Eligibility requirements from the funders, EPSRC, are stringent and require that candidates qualify as UK-domiciled for funding purposes. Information on eligibility can be found at https://www.epsrc.ac.uk/skills/students/help/eligibility/. Applications failing these eligibility criteria unfortunately cannot be accepted. Note that an ICASE award attracts an additional stipend per year.

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1 http://www.bbc.co.uk/rd/projects/audio-research-partnership
2 http://www.bbc.co.uk/rd/projects/data-science-research-partnership
3 http://www.bbc.co.uk/rd/projects/making-musical-mood-metadata 

 

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PhD Studentship in passive and active antennas and quasi-optical devices for mm-and THz applications


Application closing date: 11/02/2018 
Start date: 02/04/2018 
Research group: Antennas & Electromagnetics
Duration: 3 years
Funding available

 

Modern communication systems and special microwave applications like security tend to move towards higher frequency bands, namely, mm-, sub-mm and THz regions. Novel solutions are required to implement emerging technologies. Well known and well established microwave implementations become either unrealisable due to dimensions, material properties and other constraints or bulky and expensive, especially for integrated active and non-linear devices.

The recent advances in the Antennas and Electromagnetics Group have shown that it is possible to realise elements of mm- and sub-mm wave ranges using so-called quasi-optical networks and devices. However, deeper insight into such systems, including combined active/passive devices, e.g., antenna with amplifier, is under particular interest and is very timely.

This fully funded research studentship aims to discover new theoretical and practical realisations and design of aforementioned devices and antennas. Both theoretical and experimental results of such work would of highest interest from purely commercial, e.g., terrestrial and satellite communications, to pure research (radioastronomy) and special applications (spectroscopy, security). All applicants should hold a masters level degree at first /distinction level in electronic engineering or radiophysics. Candidates are asked to possess fundamental knowledge and skills in one or more of the following areas:

• Electromagnetic and antenna theory • Classic and long-wave (quasi) optics • Basic circuit theory with emphasis on active and non-linear operational modes • Measurement experience using a VNA or THz-TDS • Basic knowledge of spectroscopy • Basic knowledge of Physics (e.g., Lagrangian Dynamics) and Chemistry • MATHEMATICS – Applied, Mathematical Physics
Strong motivation to aim for excellence is essential, as are good communication skills. Details about the Antennas and Electromagnetics Group can be found at http://antennas.eecs.qmul.ac.uk

All nationalities are eligible to apply for this studentship. We offer a 3-years fully funded PhD studentship, with a bursary ~£16.5K/year and a fee waiver (including non-EU students), supported by the School of Electronic Engineering and Computer Science of the Queen Mary University of London, UK (www.eecs.qmul.ac.uk). The first supervisor is Dr. Rostyslav Dubrovka (http://www.eecs.qmul.ac.uk/people/view/3133/dr-rostyslav-dubrovka). In addition to the studentship, we also welcome applications from self-funded students with relevant background or experience.

To apply, please follow the on-line instructions at the college web-site for research degree applicants (HTTP://www.qmul.ac.uk/postgraduate/research/subjects/). At the page, select ‘Elctronic Engineering in the list “FIND”’ and follow the instructions on the right-hand side of the web page. Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement (no more than 500 words) should answer two questions: (i) Why are you interested in the topic described above? (ii) What relevant experience do you have?
Please attach your CV, a transcript of records, and the title/s of your MSc dissertation/s. In addition, we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: http://www.eecs.qmul.ac.uk/phd/apply.php

Please, note that the main subject of offered PhD study is “Passive and active antennas and quasi-optical devices for mm- and THz applications”. Hence, your Statement is supposed to be closely connected to the detailed list of suggested topics which can be found at Project Ideas or at http://www.eecs.qmul.ac.uk/~rostyslav/PhD_Topics.htm. Applicants seeking further information or feedback on their suitability are encouraged to contact Dr. Rostyslav Dubrovka at r.dubrovka@qmul.ac.uk with subject “THz Antennas & QO Devices PhD”. However, please, do not send documents as they will be reviewed only after the deadline.

The closing date for the applications is February 11, 2018.

Interviews are expected to take place in the end of February, 2018.

Starting date: April-May 2018 (dates can be flexible).

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PhD Studentship in Understanding Audio-visual Interactions through Multi-view Mapping


Application closing date: 31/01/2018 
Start date: 01/10/2018 
Duration: 3 years
Funding available

 

The Centre for Intelligent Sensing at Queen Mary University of London invites applications for a PhD Studentship to undertake research in the area of deep learning for scene understanding from moving and heterogeneous audio-visual sensors (e.g. body cameras). The PhD project will focus on audio and video re-identification from multiple heterogeneous devices, on recognizing actions and spotting audio-visual keywords.

All nationalities are eligible to apply for this studentship, to be ideally started in or after June 2018. The studentship is for up to four years, and covers student fees as well as a tax-free stipend.

This PhD project is part of an interdisciplinary collaboration between the Centre for Intelligent Sensing (http://cis.eecs.qmul.ac.uk) at Queen Mary University of London (QMUL) and the Centre for Information Technology (http://ict.fbk.eu) at the Fondazione Bruno Kessler (FBK), Trento, Italy. The PhD student will spend approximatively half of their time in London and half of their PhD time in Trento and will have access to state-of-the-art laboratories, including aerial and ground robotic sensors, and multi-camera and multi-microphone installations. The PhD student will be based at Centre for Intelligent Sensing in the School of Electronic Engineering and Computer Science at Queen Mary, University of London and will be supervised by Professor Andrea Cavallaro and Dr Alessio Brutti.

Candidates should have a first-class honours degree or equivalent, or a good MSc Degree, in Computer Science, Physics, Mathematics or Electronic Engineering. Candidates must be confident in applied mathematics, and should have good programming experience, in particular C/C++, Python and MATLAB environment. Previous knowledge of Signal Processing or Deep Learning/Machine Learning is required.

To apply please follow the on-line process at http://www.qmul.ac.uk/postgraduate/applyresearchdegrees/index.html by selecting Electronic Engineering or Computer Science in the A-Z list of research opportunities and following the instructions on the right hand side of the web page. Please note that instead of the 'Research Proposal' we request a 'Statement of Research Interests'. Your Statement of Research Interest should answer two questions: Why are you interested in the proposed area? What is your experience in the proposed area? In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper.

For more information and to apply, please visit: http://www.eecs.qmul.ac.uk/phd/apply.php

Informal enquiries can be made by email to Professor Andrea Cavallaro (a.cavallaro@qmul.ac.uk).

The closing date for the applications is 31 January 2018.

Interviews are expected to take place during the week commencing 12 February 2018.

 

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PhD Studentships in Electronic Engineering and Computer Science


Application closing date: 31/01/2018 
Start date: 01/10/2018 
Duration: 3 years
Funding available

 

The School of Electronic Engineering and Computer Science (EECS) at Queen Mary University of London has eight fully-funded studentships for exceptional PhD applicants to join our school. Applications will be considered in any area of Electronic Engineering and/or Computer Science matching our research interests. For general information on research in EECS and our research groups, see http://www.eecs.qmul.ac.uk/research/, and for a list of potential research projects see http://www.eecs.qmul.ac.uk/phd/research-topics/projectideas. Applicants may also propose their own topics, but they must be able to find a suitable supervisor who is willing to supervise the project. Prospective applicants are advised to contact potential supervisors to discuss project ideas and to ascertain whether the supervisor is eligible for one of these studentships.

Two types of studentships are available. Applicants of all nationalities are eligible to apply for College studentships, and applicants who satisfy the Engineering and Physical Sciences Research Council (EPSRC) residence criteria https://www.epsrc.ac.uk/skills/students/help/eligibility/ are eligible to apply for EPSRC studentships. All studentships are planned to start in Autumn 2018, and last for three (College) or three and a half (EPSRC) years, subject to satisfactory progress. Studentships cover tuition fees as well as a tax-free stipend of £16,553 per annum (2017 rate, expected to increase for 2018 and subsequent years).

Candidates must have a first-class honours degree or equivalent, and/or a good MSc degree in Computer Science, Electronic Engineering, or a related discipline. Experience in research and a track record of publications are advantageous but not a strict requirement.

The project will be based in the School of EECS, and the student will join a group of over 300 full-time PhD students, post-doctoral researchers and academics in EECS. For general enquiries contact Mrs Melissa Yeo m.yeo@qmul.ac.uk (administrative enquiries) or Professor Simon Dixon s.e.dixon@qmul.ac.uk (academic enquiries).

To apply for one of these studentships, please follow the on-line process at www.qmul.ac.uk/postgraduate/applyresearchdegrees/; click on the list of Research Degree Subjects, select ‘Electronic Engineering’ or ‘Computer Science’ in the ‘A-Z list of research opportunities’, and follow the instructions on the right-hand side of the web page. If you have a particular supervisor in mind, make sure that you indicate this person on the application form.

In your 'Research Proposal' you should describe the topic or topics that you are interested in researching (preferably after discussing these with potential supervisors), and answer the two questions: (i) Why are you interested in the proposed area? (ii) What is your experience in the proposed area? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work (e.g. a published academic paper or an excerpt from your final year dissertation). More details on the application process can be found at: http://www.eecs.qmul.ac.uk/phd/how-to-apply

The closing date for applications is 31/1/2018.

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PhD Studentship in Computer Vision & Robotics (2018)


Application closing date: 31/01/2018 
Start date: 02/04/2018 
Research group: Computer Vision 
Duration: 3 years
Funding available

 

Applications are invited for a fully funded UK/EU PhD studentship, on the topic of active vision for robotics. The successful candidate will develop vision-based robotic exploration and manipulation algorithms, for use in cluttered and low-visibility environments.

This research will be part of a new EPSRC Robotics & Artificial Intelligence Hub, addressing the management of legacy nuclear waste, which is coordinated by the National Centre for Nuclear Robotics. The PhD position will be based in the Advanced Robotics group (ARQ) at QMUL, with access to state of the art equipment for sensing and robotics (including lidar and stereo systems, a UR5 robot arm, and an Allegro robot hand). The successful candidate will be co-supervised by Dr Miles Hansard and Dr Lorenzo Jamone, in the School of EECS. There will also be opportunities to collaborate with other members of the EPSRC Hub, including Prof Gerhard Neumann at the University of Lincoln.

Candidates should have a first class honours degree or equivalent (and preferably a Masters degree) in Computer Science, Engineering or a related field. Strong programming skills in C++, Matlab, or Python are essential. Knowledge of one or more of the following is desirable: multiple view geometry, reinforcement / deep learning, optimization algorithms, kinematics, depth cameras, spatial statistics, Linux, ROS.

Informal enquiries can be made by email to Dr Miles Hansard (miles.hansard@qmul.ac.uk). To apply please follow the on-line process http://www.qmul.ac.uk/postgraduate/howtoapply/ by selecting 'Computer Science' in the 'A-Z list of research opportunities' and following the instructions on the right hand side of the web page.

Please note that instead of the 'Research Proposal' we request a 'Statement of Research Interests'. Your Statement of Research Interest should answer two questions: (i) Why are you interested in the proposed area? (ii) What is your experience in the proposed area? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: http://www.eecs.qmul.ac.uk/phd/apply.php

The closing date for applications is January 31st, 2018, followed by interviews in February. The starting date of the position will be subject to agreement.

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PhD Studentship in passive and active antennas and quasi-optical devices for mm-and THz applications


Application closing date: 15/10/2017
Start date: 03/01/2018
Research group: Antennas & Electromagnetics
Duration: 3 years
Funding available

 

Modern communication systems and special microwave applications like security tend to move towards higher frequency bands, namely, mm-, sub-mm and THz regions. Novel solutions are required to implement emerging technologies. Well known and well established microwave implementations become either unrealisable due to dimensions, material properties and other constraints or bulky and expensive, especially for integrated active and non-linear devices. The recent advances in the Antennas and Electromagnetics Group have shown that it is possible to realise elements of mm- and sub-mm wave ranges using so-called quasi-optical networks and devices. However, deeper insight into such systems, including combined active/passive devices, e.g., antenna with amplifier, is under particular interest and is very timely.

This fully funded research studentship aims to discover new theoretical and practical realisations and design of aforementioned devices and antennas. Both theoretical and experimental results of such work would of highest interest from purely commercial, e.g., terrestrial and satellite communications, to pure research (radioastronomy) and special applications (spectroscopy, security).

All applicants should hold a masters level degree at first /distinction level in electronic engineering or radiophysics. Candidates are asked to possess fundamental knowledge and skills in one or more of the following areas:

    • Electromagnetic and antenna theory
    • Classic and long-wave (quasi) optics
    • Basic circuit theory with emphasis on active and non-linear operational modes
    • Measurement experience using a VNA or THz-TDS
    • Basic knowledge of spectroscopy
    • Basic knowledge of Physics (e.g., Lagrangian Dynamics) and Chemistry
    • MATHEMATICS – Applied, Mathematical Physics

Strong motivation to aim for excellence is essential, as are good communication skills. Applicants seeking further information or feedback on their suitability are encouraged to contact Dr. Rostyslav Dubrovka at r.dubrovka@qmul.ac.uk with subject “THz Antennas & QO Devices PhD”. The suggested and more detailed list of topics can be found at http://www.eecs.qmul.ac.uk/phd/research-topics/projectideas or at http://www.eecs.qmul.ac.uk/~rostyslav/PhD_Topics.htm. Details about the Antennas and Electromagnetics Group can be found at http://antennas.eecs.qmul.ac.uk Please attach your CV, a transcript of records, and the title/s of your MSc dissertation/s.

All nationalities are eligible to apply for this studentship. We offer a 3-years fully funded PhD studentship, with a bursary ~£16.5K/year and a fee waiver (including non-EU students), supported by the School of Electronic Engineering and Computer Science of the Queen Mary University of London, UK (www.eecs.qmul.ac.uk). The first supervisor is Dr. Rostyslav Dubrovka. In addition to the studentship, we also welcome applications from self-funded students with relevant background or experience.

To apply, please follow the on-line process (www.qmul.ac.uk/postgraduate/apply) by selecting ‘Computer Science in the ‘A-Z list of research opportunities’ and following the instructions on the right-hand side of the web page. Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement (no more than 500 words) should answer two questions: (i) Why are you interested in the topic described above? (ii) What relevant experience do you have? In addition, we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: http://www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is October 15, 2017.

Interviews are expected to take place in the end of October, 2017.

Starting date: January 2017 (dates can be flexible).

 

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PhD Studentship in Dynamic Adaptive Automated Software Engineering


Application closing date: 31/08/2017 Closed
Start date: 02/10/2017
Research group: Theoretical Computer Science (Theory)
Duration: 3 years
Funding available

 

The Operational Research Group (http://or.eecs.qmul.ac.uk/) within the School of Electronic Engineering and Computer Science, Queen Mary University of London (QMUL), invites applications for a fully-funded PhD studentship to work on a project funded by the Engineering and Physical Sciences Research Council.

TheDAASE Project (http://daase.cs.ucl.ac.uk/about/) is an EPRSC funded project involving QMUL, University College London, The University of Birmingham, The University of Stirling and The University of Sheffield. Our industrial partners include Berner and Mattner, BT Laboratories, Ericsson, GCHQ, Honda Research Institute Europe, IBM, Microsoft Research and Motorola UK. It involves around 50 academics working over 6 years with £6.8M funding.

Search Based Software Engineering (SBSE) aims to automatically build better software using computational search techniques, and draws from optimisation, machine learning and operational research. Specifically we aim to improve processes to build and maintain source code using techniques that operate on representations of source code. These techniques could include genetic programming and evolutionary computation, or more broadly any metaheuristic, machine learning or operational research technique. Examples of the type of software we would like to improve include commonly used algorithms  in mathematics, computer science, digital signal processing  and operational research.

The successful candidate will pursue a course of research investigating the application of computational search methods, such as evolutionary computation and meta/hyper-heuristics, to software engineering challenges with a focus on real-world applications.

DAASE is a highly collaborative project involving 5 UK universities. The successful candidate will have opportunities to visit and work with industrial and other partners and to be fully engaged with the international community through conferences, workshops and other networking activities. This will enhance their training and development and open new opportunities for collaboration and intellectual development. Students will also have the opportunity to engage with researchers within the OR group working on other projects in a variety of application domains.

All nationalities are eligible to apply for this studentship, which will start on 1st October 2017 or as soon as possible thereafter. The studentship is for three years, and covers Home/EU student fees as well as a tax-free stipend of around £16,500 per annum.

Candidates are expected to have a first class honors degree or Masters in Computer Science, Mathematics, Operations Research or related discipline, from a UK University or an equivalent standard from an overseas university. The successful candidate must have a strong programming background, as well as good analytical and communication skills. The student is expected to work as part of a team and independently, and to prepare clear reports and research papers. An understanding of mathematical optimization techniques, heuristic and hyper-heuristic search is highly desirable although not mandatory.

Informal enquiries can be made by email to Dr. John Drake (j.drake@qmul.ac.uk) who will supervise the project alongside Prof. Edmund Burke. Informal enquiries are strongly encouraged before a candidate submits an application.

For more information and to apply, please visit: http://www.eecs.qmul.ac.uk/phd/apply.php.

 

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Data analytics for video distribution applications


Application closing date: 04/08/2017 Closed
Start date: 02/10/2017
Research group: Multimedia & Vision (MMV)
Duration: 3 years 
Funding available

 

Applications are invited for a PhD Studentship, to undertake research in the area of data analytics for video distribution applications in collaboration with The BBC from September 2017, or as soon as possible thereafter. The studentship is based at the School of Electronic Engineering and Computer Science at Queen Mary, University of London and will be supervised by Prof. Ebroul Izquierdo. The studentship will involve the development of new techniques aiming at effectively exploiting data analytics for improved content delivery. Specifically, research on highly efficient video compression technology and related enabling approaches for optimised content for delivery are targeted. Underpinning this PhD research work should be methods relying on quality of experience metrics to ensure that the results provide highly enhanced quality of experience to the end user. It is expected that the students will work in close collaboration with other researchers, both in QMUL and The BBC.

Candidates should have a first class honours degree or equivalent (and preferably a Masters Degree) in any relevant area including Computer Science, Electronic Engineering, Mathematics, Physics, or a related field, and the ability to demonstrate strong mathematical and analytical skills. Good programming skills and background in video processing are also desirable. This studentship, funded by an EPSRC ICase, is for fees plus a tax-free stipend starting at approx. £16.5K per annum. It is a CASE award and attracts an additional stipend from the industrial partner. Further details of the EPSRC scheme including terms and conditions can be found here: https://www.epsrc.ac.uk/skills/students/

Applicants must satisfy UK residence requirements as defined here: https://www.epsrc.ac.uk/skills/students/help/eligibility/

Informal enquiries can be made by email to Prof. Ebroul Izquierdo at ebroul.izquierdo@qmul.ac.uk.To apply please follow the on-line process (see www.qmul.ac.uk/postgraduate/apply/) by selecting “Electronic Engineering” in the “A-Z list of research opportunities” and following the instructions on the right hand side of the web page.

Please note that instead of the 'Research Proposal' we request a 'Statement of Research Interests'. Your Statement of Research Interest should answer two questions: (i) Why are you interested in the proposed area? (ii)What is your experience in the proposed area? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: http://www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 4th August 2017

.

Interviews are expected to take place after the 7th August 2017.

 

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Two PhD Studentships in Music Data Science at the Centre for Digital Music


Application closing date: 30/06/2017 Closed
Start date: 02/10/2017
Research group: Centre for Digital Music (C4DM)
Duration: 3.5 years
Funding available

 

Funding is available for two PhD positions to study and research in the world-renowned Centre for Digital Music at Queen Mary University of London. Project 1 is an EPSRC ICASE studentship with BBC R&D in Data Science; Project 2 is a QMUL funded studentship associated with the EPSRC-funded Programme Grant, Fusing Audio and Semantic Metadata for Intelligent Music Production and Consumption (see semanticaudio.ac.uk).

The closing date for applications is 30 June 2017, and interviews are expected to take place around the middle of July, with an intended start date of late September 2017. Enquiries may be addressed to mark.sandler@qmul.ac.uk.

Full details are available for download from http://bit.ly/2ozkLI5

 

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Studentship in Computer based histopathological image analysis for cancer grading and progression


Application closing date: 23/06/2017 Closed
Start date: 02/10/2017
Research group:
Duration: 3 years 
Funding available

 

The assessment of pathological cancer regression after preoperative chemotherapy is mostly based on the assessment of tumour morphological features, such as the proportion of cancer cells in relation to the total tumour region, as well as biologically relevant histology features, such the tumour invasion front. Currently, this histopathological evaluation is performed by expert pathologists through visual assessment of the tumour microscopic slides. This is often time-consuming, expensive and may be unacceptably inconsistent and imprecise. This project aims at developing an intelligent system that enables automatic, precise, objective and reproducible assessment of tumour regression and precise characterisation of the tumour invasion front based on the digital scans of resected tumour tissue slides, by integrating beyond the state-of-the-art, specifically designed computer vision, image processing and machine learning schemes.

All applicants should hold an MSc degree (or BSc with relevant experience) in computer science, statistics, mathematics, or engineering with good programming skills. Applicants with advanced knowledge in areas such as machine learning and computer vision are encouraged to apply. Strong motivation to aim for excellence is essential, as are excellent communication skills. Applicants seeking further information or feedback on their suitability are encouraged to contact Dr. Qianni Zhang at qianni.zhang@qmul.ac.uk. Please attach your CV, a transcript of records, and the title/s of your BSc/MSc dissertation/s

.

All nationalities are eligible to apply for this studentship. We offer a 3-years fully funded PhD studentship, with a bursary ~£16.5K/year and a fee waiver (including non-EU students), supported by the School of Electronic Engineering and Computer Science of the Queen Mary University of London, UK (www.eecs.qmul.ac.uk). The primary PhD supervisors is Dr Qianni Zhang. In addition to the studentship, we also welcome applications from self-funded students with relevant background or experience. To apply, please follow the on-line process (www.qmul.ac.uk/postgraduate/apply) by selecting ‘Computer Science in the ‘A-Z list of research opportunities’ and following the instructions on the right-hand side of the web page. Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement (no more than 500 words) should answer two questions: (i) Why are you interested in the topic described above? (ii) What relevant experience do you have? In addition, we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: http://www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 23 June, 2017.

Interviews are expected to take place in early July 2017.

Starting date: before September 2017 (dates can be flexible).

 

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PhD Studentship in the measurement of user quality of experience in broadband networks


Application closing date: 14/06/2017 Closed
Start date: 02/10/2017
Research group: Networks
Duration: 3 years 
Funding available

 

The users perceived Quality of Experience (QoE) drives churn in the broadband networks industry - good QoE plays a large part in the retention of customers. The users QoE is known to be affected by the quality of service (QoS) factors: packet loss probability, packet delay and packet delay jitter.

Earlier research has shown how to to estimate QoE from QoS, and also that the relationship between QoS and QoE is non-linear, and varies from application to application. However statistical errors in sampling QoS metrics will always result in levels of imprecision and inaccuracy in the estimated QoE.

This fully funded research proposal aims to discover the means by we can evaluate the summed effects of very different levels and types of statistical error in the QoS measurements, and evaluate the effect of these different errors on the predicted QoE. Assuming standard probing, the errors in the measurements can be large enough to be significant: error in evaluated QoS could be such as to result in operators concluding that the network provides ‘EXCELLENT’ QoE when the QoE is really ‘POOR’ or even ‘BAD’. This has considerable commercial significance.

All applicants should hold at least a BSc in electronic engineering, computer science, statistics or mathematics. Strong motivation to aim for excellence is essential, as are good communication skills. Applicants seeking further information or feedback on their suitability are encouraged to contact Dr. John Schormans at j.schormans@qmul.ac.uk with subject “Networks-QoE PhD”. Please attach your CV, a transcript of records, and the title/s of your BSc/MSc dissertation/s.

All nationalities are eligible to apply for this studentship. We offer a 3-years fully funded PhD studentship, with a bursary ~£16.5K/year and a fee waiver (including non-EU students), supported by the School of Electronic Engineering and Computer Science of the Queen Mary University of London, UK (www.eecs.qmul.ac.uk). The first supervisor is Dr John Schormans. In addition to the studentship, we also welcome applications from self-funded students with relevant background or experience.

To apply, please follow the on-line process (www.qmul.ac.uk/postgraduate/apply) by selecting ‘Computer Science in the ‘A-Z list of research opportunities’ and following the instructions on the right-hand side of the web page. Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement (no more than 500 words) should answer two questions: (i) Why are you interested in the topic described above? (ii) What relevant experience do you have? In addition, we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: http://www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is June 14, 2017

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Interviews are expected to take place in mid July 2017.

Starting date: before October 2017 (dates can be flexible).

 

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PhD Studentship in deep learning for mobile camera networks


Application closing date: 15/05/2017 Closed
Start date: 01/06/2017
Research group: Centre for Intelligent Sensing
Duration: 4 years 
Funding available

 

The Centre for Intelligent Sensing at Queen Mary University of London invites applications for a PhD Studentship to undertake research in the area of machine learning and mobile computer vision for scene monitoring with multiple collaborative robotic or wearable cameras. The PhD project will focus on machine learning methods for people detection and tracking, scene analysis and activity recognition for assistive technologies.

All nationalities are eligible to apply for this studentship, to be started in or after June 2017. The studentship is for up to four years, and covers student fees as well as a tax-free stipend.

This PhD project is part of an interdisciplinary collaboration between the Centre for Intelligent Sensing (http://cis.eecs.qmul.ac.uk) at Queen Mary University of London (QMUL) and the Centre for Information Technology (http://ict.fbk.eu) at the Fondazione Bruno Kessler (FBK), Trento, Italy. The PhD student will spend approximatively half of their time in London and half of their PhD time in Trento and will have access to state-of-the-art laboratories, including aerial and ground robotic sensors, a multi-camera installation at a large open hallway and a smart home facility equipped with multiple cameras. The PhD student will be based at Centre for Intelligent Sensing in the School of Electronic Engineering and Computer Science at Queen Mary, University of London and will be supervised by Professor Andrea Cavallaro and Dr Oswald Lanz.

Candidates should have a first-class honours degree or equivalent, or a good MSc Degree, in Computer Science, Physics, Mathematics or Electronic Engineering. Candidates must be confident in applied mathematics, and should have good programming experience, in particular of C/C++ language and of MATLAB environment. Previous knowledge of Robotic Vision or Distributed Signal Processing or Deep Learning/Machine Learning is required.

To apply please follow the on-line process at http://www.qmul.ac.uk/postgraduate/applyresearchdegrees/index.html by selecting Electronic Engineering or Computer Science in the A-Z list of research opportunities and following the instructions on the right hand side of the web page. Please note that instead of the 'Research Proposal' we request a 'Statement of Research Interests'. Your Statement of Research Interest (no more than 500 words or one side of A4 paper) should state whether you are interested in a computer vision PhD project, or an audio processing PhD project, or an audio-visual processing PhD project. Moreover, your Statement of Research Interest should answer two questions: Why are you interested in the proposed area? What is your experience in the proposed area? In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper.

For more information and to apply, please visit: http://www.eecs.qmul.ac.uk/phd/apply.php

Informal enquiries can be made by email to Professor Andrea Cavallaro (a.cavallaro@qmul.ac.uk).

The closing date for the applications is 15 May 2017.

Interviews are expected to take place during the week commencing 22 May 2017.

 

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PhD Studentships in Electronic Engineering and Computer Science


Application closing date: 28/04/2017 Closed
Start date: 02/10/2017
Duration: 3 years 
Funding available

 

The School of Electronic Engineering and Computer Science at Queen Mary University of London has three fully-funded studentships for exceptional PhD applicants to join our school. Applications will be considered in any area of Electronic Engineering and/or Computer Science matching our research interests. For general information on research in EECS and our research groups, see http://www.eecs.qmul.ac.uk/research/, and for a list of potential research projects see http://www.eecs.qmul.ac.uk/phd/research-topics/projectideas. Applicants may also propose their own topics, but they must be able to find a suitable supervisor who is willing to supervise the project. Prospective applicants are advised to contact potential supervisors to discuss project ideas and to ascertain whether the supervisor is eligible for one of these studentships.

Applicants of all nationalities are eligible to apply for these studentships, which are planned to start in Autumn 2017. The studentship is for three years, and covers tuition fees as well as a tax-free stipend starting at £16,553 per annum.

Candidates must have a first-class honours degree or equivalent, and/or a good MSc degree in Computer Science, Electronic Engineering, or a related discipline. Experience in research and a track record of publications are advantageous but not a strict requirement.

The project will be based in the School of EECS, and the student will join a group of over 300 full-time PhD students, post-doctoral researchers and academics in EECS. For general enquiries contact Mrs Melissa Yeo m.yeo@qmul.ac.uk (administrative enquiries) or Dr Simon Dixon s.e.dixon@qmul.ac.uk (academic enquiries).

To apply for one of these studentships, please follow the on-line process at www.qmul.ac.uk/postgraduate/applyresearchdegrees/; click on the list of Research Degree Subjects, select ‘Electronic Engineering’ or ‘Computer Science’ in the ‘A-Z list of research opportunities’, and follow the instructions on the right-hand side of the web page. If you have a particular supervisor in mind, make sure that you indicate this person on the application form.

In your 'Research Proposal' you should describe the topic or topics that you are interested in researching (preferably after discussing these with potential supervisors), and answer the two questions: (i) Why are you interested in the proposed area? (ii) What is your experience in the proposed area? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work (e.g. an excerpt from your final year dissertation or a published academic paper). More details on the application process can be found at: http://www.eecs.qmul.ac.uk/phd/how-to-apply

The closing date for applications is 28/4/2017.

 

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PhD Studentship in Bayesian Artificial Intelligence for Decision Making Under Uncertainty


Application closing date: 14/04/2017 Closed
Start date: 02/10/2017
Research group: Risk and Information Management (RIM)
Duration: 3 years 
Funding available

 

Do you enjoy working with probabilities, data, and algorithms? Are you interested in the theory of causality? Do you want to improve the methods we use to discover real-world causal, or otherwise, relationships from data and knowledge? Are you interested in developing mathematical models that simulate real-world events for prediction, risk management and decision making purposes?

The PhD student will specialise in the theory and application of Bayesian networks (BNs), with a focus on structure learning with causal discovery (i.e. learning the cause-and-effect structure, or otherwise, of a problem). The project will be adjusted to the skills and interests of the successful candidate. For example, the project could be extended to game theoretic problems from decision sciences, and theoretical advancements will be assessed by applying them to an area (or areas) of your interest, preferably from economics, finance, medicine, project management, property market, and forensics.

All applicants should hold an MSc degree (or BSc with relevant experience) in computer science, statistics, mathematics, engineering, physics, or economics with strong maths component. Applicants with advanced knowledge in areas such as statistical machine learning and probability theory are particularly encouraged to apply. Strong motivation to aim for excellence is essential, as are excellent communication skills. Applicants seeking further information or feedback on their suitability are encouraged to contact Dr. Anthony Constantinou at a.constantinou@qmul.ac.uk with subject “BAYES-AI PhD”. Please attach your CV, a transcript of records, and the title/s of your BSc/MSc dissertation/s.

All nationalities are eligible to apply for this studentship. We offer a 3-years fully funded PhD studentship, with a bursary ~£16.5K/year and a fee waiver (including non-EU students), supported by the School of Electronic Engineering and Computer Science of the Queen Mary University of London, UK (www.eecs.qmul.ac.uk). PhD supervisors are: Dr Anthony Constantinou (http://www.constantinou.info) and Prof Norman Fenton (http://www.eecs.qmul.ac.uk/~norman/). In addition to the studentship, we also welcome applications from self-funded students with relevant background or experience.

To apply, please follow the on-line process (www.qmul.ac.uk/postgraduate/apply) by selecting ‘Computer Science in the ‘A-Z list of research opportunities’ and following the instructions on the right-hand side of the web page. Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement (no more than 500 words) should answer two questions: (i) Why are you interested in the topic described above? (ii) What relevant experience do you have? In addition, we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: http://www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is April 14, 2017.

Interviews are expected to take place in mid May 2017.

Starting date: before October 2017 (dates can be flexible).

 

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Two PhD Studentships in Medical Decision Support using Bayesian Networks


Application closing date: 24/03/2017 Closed
Start date: 02/10/2017
Research group:
Duration: 3 years
Funding available

 

How would you like to play a key role in a major new collaborative project that has the potential to improve the well-being of millions of people? The project, called PAMBAYESIAN (Patient Managed Decision-Support using Bayesian Networks) aims to develop a new generation of intelligent medical decision support systems, applicable to home-based and wearable real-time monitoring systems for chronic conditions including rheumatoid arthritis, diabetes in pregnancy and atrial fibrillation. The project team includes world-leading researchers from both the School of Electronic Engineering and Computer Science (EECS) and clinical academics from the Barts and the London School of Medicine and Dentistry (SMD). The collaboration is underpinned by extensive research in EECS and SMD, with access to digital health firms that have experience developing patient engagement tools for clinical development. The collaboration with such organisations ensures that there will be excellent future career opportunities for those candidates who ultimately wish to work in the private, rather than public, sector.

 

The PhD students will focus on developing a Bayesian network (BN) decision-support model for one of the chronic medical conditions, taking account of relevant data and expert judgment. The work is very inter-disciplinary and successful candidates will work with a clinician with expertise in the relevant medical areas, as part of a large team considering the challenges of embedding BN medical decision support solutions into small devices. Further details about the project are at www.eecs.qmul.ac.uk/~norman/projects/PAMBAYESIAN.

We offer a 3-years fully funded PhD studentship, with a bursary ~£16.5K/year and a fee waiver (including non-EU students), supported by the School of Electronic Engineering and Computer Science of the Queen Mary University of London, UK (www.eecs.qmul.ac.uk). PhD supervisors are: Prof Norman Fenton (http://www.eecs.qmul.ac.uk/~norman/) and Dr William Marsh (http://www.eecs.qmul.ac.uk/~william/) In addition to the bursary opportunity, the research group would also welcome applications for self-funded students and encourages applicants to contact relevant potential supervisors to discuss their research proposals. All nationalities are eligible to apply for this studentship. All applicants should hold a BSc Class I or 2i degree a relevant discipline (maths, computer science, statistics, engineering) with advanced knowledge in areas such as decision support systems, Bayesian reasoning and probability theory, or knowledge elicitation. Programming skills are also desirable, but not essential. We will provide necessary training and continual professional development. Strong motivation to aim for excellence is essential, as are excellent communication skills.

 

Applicants seeking further information or feedback on their suitability are encouraged to email Prof Norman Fenton (n.fenton@qmul.ac.uk) and Dr William Marsh (d.w.r.marsh@qmul.ac.uk) with subject “PamBayesian PhD: ”, including: a) your full CV; b) transcript of results; c) a cover letter (i.e. motivation statement) of 1 page maximum.

To apply, please follow the on-line process (www.qmul.ac.uk/postgraduate/apply) by selecting ‘Computer Science in the ‘A-Z list of research opportunities’ and following the instructions on the right-hand side of the web page. Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement (no more than 500 words) should answer two questions: (i) Why are you interested in the topic described above? (ii) What relevant experience do you have? In addition, we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: http://www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 24/03/17.

Interviews are expected to take place in April 5-6 2017.

Starting date: before October 2017 (dates can be flexible).

 

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PhD Studentship in Intelligent Robotics


Application closing date: 15/02/2017 Closed
Start date: 02/10/2017
Duration: 3 years 
Funding available

 

Can you imagine a world where humans and robots are seamlessly integrated and can effectively cooperate? Do you want an active role in shaping this incoming robotic revolution? Are you interested in unraveling the mysteries of human intelligence, and equipping robots with advanced human-like abilities?

We offer a 3-years fully funded PhD studentship in intelligent robotics, with a bursary ~£16.5K/year and a fee waiver (including non-EU students), supported by the School of Electronic Engineering and Computer Science of the Queen Mary University of London, UK (www.eecs.qmul.ac.uk). Supervisor: Dr. Lorenzo Jamone (http://lorejam.blogspot.co.uk/).

Starting date: before October 2017 (dates can be flexible).

You will have the chance to work on exciting topics such as advanced robotic manipulation, robot learning, tactile sensing, human-robot interaction, cognitive robotics, using state-of-the-art robotic platforms and interacting with other researchers in a vibrant and multidisciplinary research environment

.

All nationalities are eligible to apply for this studentship. All applicants should hold a MS degree (or a BSc with proven experience in robotics) in electrical and computer engineering, computer science, mechanical engineering, or a field closely related to robotics. Strong motivation to aim for excellence is essential. Very good programming skills (C/C++, Java, Python) are also required. Applicants with advanced knowledge of robotics, control, machine learning, artificial intelligence, and/or previous research experience in these fields, are particularly encouraged to apply.

In the first instance please contact Dr. Lorenzo Jamone (l.jamone@qmul.ac.uk), with subject "QMUL PhD robotics: "), including: A) your full CV; B) transcript of records; C) a cover letter (i.e. motivation statement) of 1 page maximum; D) at least two academic references.

To apply, please follow the on-line process (www.qmul.ac.uk/postgraduate/apply) by selecting ‘Computer Science in the ‘A-Z list of research opportunities’ and following the instructions on the right-hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement should answer two questions: (i) Why are you interested in the topic described above? (ii) What relevant experience do you have? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition, we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: http://www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 15/02/17.

 

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PhD Studentship in Human-Robot Interaction, Haptics and/or VR


Application closing date: 15/02/2017 Closed
Start date: 02/10/2017
Duration: 3 years 
Funding available

 

Applications are invited for a fully funded PhD studentship at Centre for Advanced Robotics (AQR) in the Queen Mary University of London, UK. The research is in the general area of human-robot interaction, haptics, wearable technologies and virtual reality. Students with more specific research ideas are welcome to apply.

All nationalities are eligible to apply for this studentship, which will ideally start in Autumn 2017. The studentship is for three years, and covers student fees as well as a tax-free stipend of £16,296 per annum.

Candidates must have a first-class honors degree or equivalent, or a good MSc Degree in Engineering/Science or equivalent. Good knowledge and skills in robotics, control, mechatronics and programming are required (Bachelor/Master/Engineering degree).

In the first instance please contact Dr Ildar Farkhatdinov (i.farkhatdinov@qmul.ac.uk, subject "QMUL PhD application robotics: ") with your CV/cover letter and transcript as PDF. Dr Ildar Farkhatdinov will lead the PhD alongside Professor Kaspar Althoefer and Dr Lorenzo Jamone.

To apply, please follow the on-line process (www.qmul.ac.uk/postgraduate/apply) by selecting ‘Computer Science in the ‘A-Z list of research opportunities’ and following the instructions on the right-hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement should answer two questions: (i) Why are you interested in the topic described above? (ii) What relevant experience do you have? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition, we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: http://www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 15/02/17.

Interviews are expected to take place in late February 2017.

 

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