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PhD fees and funding

Finding funding is an important element of the application process and we recommend that you begin to explore your funding options as early as possible.

There are a range of different sources of funding available for PhD students at Queen Mary – in fact, we offer 220 fully or partially funded studentships. A PhD studentship is a funded postgraduate studentship opportunity either attached to a specific research project or open to postgraduate research applicants in a specific field or academic school.

Applications are open for the following PhD Studentships in the School of Electronic Engineering and Computer Science:

PhD titleResearch groupSupervisorClosing date

PhD Studentship in Machine Listening

Centre for Digital Music and Centre for Intelligent Sensing Dr. Emmanouil Benetos 06/07/2018

PhD Studentship in Computational Linguistics (2018)

Cognitive Science and Games AI   Prof.  Massimo Poesio  10/07/2018

PhD Studentship in Bayesian Artificial Intelligence for Decision Making Under Uncertainty

Risk and Information Management Dr. Anthony Constantinou 27/07/2018
PhD Studentship in Automated Software Engineering

Operational Research

Dr. John Drake

Dr. John Woodward

31/07/2018

QMUL/EPSRC PhD Studentships

Antennas & Electromagnetics Prof. Yang Hao 10/08/2018

PhD Studentship in Machine Learning for Automated Software Engineering


Operational Research

Dr. John Drake, Dr. John Woodward, Prof. Edmund Burke


Early applications advised

 

Other studentship opportunities

You can also search for studentships on the Queen Mary where you can filter your search via study level, school, country. At the School of Electronic Engineering and Computer Science, we receive a number of applications from the following schemes each year:

 

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PhD Studentship in Machine Listening


Application closing date: 06/07/2018
Start date: Autumn 2018
Research group: Centre for Digital Music and Centre for Intelligent Sensing

Duration: 3 years
Funding available

Applications are invited for a fully-funded PhD studentship in Machine Listening / Computer Audition within the School of Electronic Engineering and Computer Science at Queen Mary University of London, to conduct research in the area of computational sound scene analysis. This research will investigate and prototype tools for sound event detection and audio context recognition from everyday sound scenes. This PhD position is linked with the EPSRC-funded project “Integrating sound and context recognition for acoustic scene analysis” on developing technologies for context-aware sound recognition. The successful candidate will investigate, propose and develop machine learning and digital signal processing methods for sound recognition, suitable for complex and time-varying acoustic environments.

All nationalities are eligible to apply for this studentship, which will start in Autumn 2018. The studentship is for three years, and covers student fees as well as a tax-free stipend of £16,777 per annum.

Candidates must have a first-class honours degree or equivalent, and/or a good MSc Degree in Computer Science, Electronic Engineering, Audio/Music Technology, Acoustics, or a related discipline. Candidates should have good programming experience in Python, Matlab, C/C++ or similar. Knowledge of machine learning and/or digital signal processing is desirable. Experience in research and a track record of publications is advantageous. There is scope to tailor the research to the interests and skills of the successful candidate.

The PhD supervisor will be Dr Emmanouil Benetos. This project is based in the Centre for Digital Music (C4DM) and Centre for Intelligent Sensing (CIS) of Queen Mary University of London. C4DM is a world-leading multidisciplinary research group in the field of Digital Music & Audio Technology; CIS has highly reputed research expertise in multi-sensor data processing, distributed signal processing, vision and audio analysis. Both groups are part of the School of Electronic Engineering and Computer Science (EECS). Details about the School can be found at http://www.eecs.qmul.ac.uk; details about C4DM at http://c4dm.eecs.qmul.ac.uk; and details about CIS at http://cis.eecs.qmul.ac.uk/ . Informal enquiries about the studentship can be made by email to Dr Benetos.

To apply, please follow the on-line process; 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 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 (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 the applications is 6/07/2018.
Interviews are expected to take place in mid-July 2018.  

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PhD Studentship in Computational Linguistics


Application closing date: 10/07/2018
Start date: subject to agreement
Research group: Joint project with Cognitive Science and Games AI

Duration: 3-3.5 years
Funding available

Applications are invited for a PhD studentship funded by a 5-year European Research Council (ERC) Advanced Grant awarded to Prof. Massimo Poesio called DALI: Disagreements in Language Interpretation.  The project will address a fundamental question for computational linguistics, psycholinguistics and theoretical linguistics: namely, the extent to which humans disagree on language interpretation (in particular, on the interpretation of anaphoric expressions), and the implications of such disagreements, e.g., on the development of computational models of anaphoric interpretation. We will do this by using online games-with-a-purpose to collect numerous interpretations for very large numbers of anaphoric expressions, building on the successful experience with the Phrase Detectives GWAP; by analyzing these data using Bayesian annotation models; and by developing computational methods  for anaphora resolution taking disambiguation into account.

The objective of the PhD studentship is to tackle one of the many problems in anaphora resolution unearthed in the anaphoric data already collected through the Phrase Detectives game and by the new games that will be developed in the project. We are looking for outstanding research potential and a background  in machine  learning, as well as, ideally. familiarity with computational semantics and computational approaches to discourse.  

Candidates should have an outstanding research potential and a background in Computational Linguistics achieved first class honours degree or equivalent (and preferably a Masters degree) in Computer Science, Computational Linguistics, Linguistics, or a related field. Familiarity with computational and/or formal semantics and with computational and linguistic approaches to anaphora strongly preferred.  Strong programming skills in Python or R crucial.

The PhD position will be joint between the Cognitive Science and the Games and AI Research Groups in EECS. The successful candidate will be supervised by Prof. Massimo Poesio, but will operate as part of a large research group including both researchers working on DALI and members of the Computational Linguistics group.

Informal enquiries can be made by email to Prof. Massimo Poesio. To apply please follow the on-line process 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 July 10th, 2018, followed by interviews end of July.
The starting date of the position will be subject to agreement.

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PhD Studentship in Bayesian Artificial Intelligence for Decision Making Under Uncertainty


Application closing date: 27/07/2018
Start date: October 2018
Research group: Risk and Information Management

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 causal, or other, relationships from data? Are you interested in algorithms that discover the Bayesian Network (BN) graph for causal inference, and the Bayesian Decision Network (BDN) graph to maximise utility and minimise risk?

The PhD student will specialise in the theory and application of BNs/BDNs, with a focus on structure learning (i.e. learning graphical models). The project will be adjusted to the skills and interests of the successful candidate. For example, theoretical advancements could be assessed by applying them to an area (or areas) of your interest, preferably from economics, finance (excluding stock market), medicine, or gaming.

All applicants should hold, or close to completing, an MSc degree (or BSc with relevant experience) in an area related to computer science, statistics, or mathematics. Applicants with advanced knowledge in areas such as statistical/probabilistic machine learning are particularly encouraged to apply. Strong motivation to aim for excellence is essential, as are excellent communication skills.
The PhD studentship is part of the EPSRC project on Bayesian Artificial Intelligence for Decision Making under Uncertainty. You can read more about the project at: www.researchgate.net/publication/325848089_Bayesian_Artificial_Intelligence_for_Decision_Making_under_Uncertainty. Applicants seeking further information or feedback on their suitability are encouraged to contact Dr. Anthony Constantinou with subject “Bayesian-AI PhD”. Please attach your CV, a transcript of records, and 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. PhD supervisor: Dr Anthony Constantinou. 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 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: www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is July 27, 2018.
Interviews are expected to take place in August 2018.
Starting date: preferably before October 2018 (date can be flexible).

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PhD Studentship in Automated Software Engineering


Application closing date: 31/07/2018
Start date: 1 October 2018
Research group: Operational Research

Duration: 3 years
Funding available

The Operational Research Group within the School of Electronic Engineering and Computer Science, Queen Mary University of London (QMUL), invites applications for a fully-funded PhD studentship.
 
The project will examine how machine learning and optimisation methods can be used to automatically improve software. The successful candidate will pursue a course of research investigating the application of computational search methods, to software engineering challenges with a focus on real-world applications. These techniques could include genetic programming and evolutionary computation, or more broadly any metaheuristic, machine learning or operational research technique.
 
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 2018. The studentship is for three years, and covers fees as well as a tax-free stipend of around £16,777 per annum.
 
Candidates are expected to have a first class honors degree or Masters in Computer Science, Mathematics, Operational 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. Your application should contain a one-page outline of research proposal stating what you would like to achieve and how you would like to achieve it. The knowledge of machine learning methods and optimisation techniques is essential. A good mathematical background is desirable. You should also state where you saw the original advertisement for the position.

Informal enquiries can be made by email to Dr. John Woodward. The PhD candidate will be supervised alongside Dr. John Drake . 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. The closing date for applications is 31st July 2018.

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QMUL/EPSRC PhD Studentships


Application closing date: 10/08/2018
Start date: 1 September 2018 (or as soon as possible thereafter)
Research group: Antennas and Electromagnetics

Duration: 3.5 years
Funding available

Applications are invited for up to eight PhD Studentships, to undertake research in the area of smart/tunable materials, nanotechnologies, antennas and electromagnetics in collaboration with UK industries including Qinetiq, Thales UK and Huawei Technologies from September 2018, or as soon as possible thereafter. The studentships are based at the School of Electronic Engineering and Computer Science at Queen Mary, University of London and will be supervised by Prof. Yang Hao (FIEEE) of the Antennas and Electromagnetics Group and other colleagues across the Faculty of Science and Engineering. The studentships will involve the development of machine-learning algorithms in material modelling and control system designs. It is expected that the students will work in close collaboration with other postdoctoral researchers funded by the EPSRC ANIMATE project on Software Defined Materials under EP/R035393/1, both in QMUL and partner industry.


Candidates should have a first or upper second class honours degree or equivalent (and preferably a Masters Degree) in any relevant area including Electronic and Computer Engineering, Mathematics, Physics, Material Science or a related field, and the ability to demonstrate strong mathematical and analytical skills. Good programming skills and background in electromagnetics are also desirable.


One of these studentships, funded by Thales UK and an EPSRC Doctoral Training Account, is for fees plus a tax-free stipend starting at £15,726 per annum. It is a CASE award and attracts an additional stipend of £5,200 per annum from the industrial partner. Further details of the EPSRC scheme including terms and conditions can be found here: https://epsrc.ukri.org/skills/students/. Due to funding restrictions applicants must satisfy UK residence requirements as defined here: www.epsrc.ac.uk/skills/students/help/Pages/eligibility.aspx


Other studentships will be available for all candidates including European and overseas students. We are valuing diversity and committed to equality.
Informal enquiries can be made by email to Prof. Yang Hao. To apply please follow the on-line process 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/how-to-apply.php

     
Interviews are expected to take place between August 23 and 31, 2018.

 

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ICASE PhD STUDENTSHIP in Network Design with BT


Application closing date: 25/05/2018
Start date: 1 July 2018 (or as soon as possible thereafter)
Research group: Operational Research

Duration: 3 years
Funding available

The Operational Research Group within the School of Electronic Engineering and Computer Science, Queen Mary University of London (QMUL), invites applications for a fully-funded PhD Studentship to undertake research in the area of Network Design Optimisation in collaboration with BT University Research. The studentship will start from 1st July 2018, or as soon as possible thereafter. The studentship will be based at the School of Electronic Engineering and Computer Science at QMUL and will be supervised by Prof Edmund Burke, Dr Jun Chen and Dr John Drake.

The studentship will investigate applications for network design, e.g. a town’s fibre network. The goal of the project is to explore software solutions to find optimal solutions to a graph problem described in a human readable declarative constraint language. It has two parts.

  • Design a declarative language for representing constraints on a graph, and the costs of the graph.
  • Implement a heuristic search engine which combines heuristic search techniques with constraint satisfaction methods to limit the search space. It is expected that the students will work in close collaboration with other researchers, both in QMUL and BT University Research.

Candidates should have a first class honours degree or equivalent (and preferably a Masters Degree) in Computer Science or a related field, and the ability to demonstrate mathematical and analytical skills. Strong programming skills and background in heuristics, metaheuristics, constraint handling and graph theory are also desirable. Successful applicants should have a clear commitment to research and should enjoy working at the intersection of theory and practice.

This studentship, funded by an EPSRC ICase, is for tuition fees plus a tax-free stipend starting at approximately £16.5K per annum. It 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 are strongly advised before application, and can be made by email to Dr Jun Chen and Dr John Drake .

To apply please follow the on-line process (see 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 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/how-to-apply/

The closing date for applications is 25 May 2018.      

Interviews are expected to take place after 01 June 2018.

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EPSRC PhD Industrial CASE STUDENTSHIP


Application closing date: 16/03/18
Start date: June 2018 
Research group: Operational Research

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 undertake research in the area of Network Design Optimisation in collaboration with BT University Research. The studentship will start from 1st June 2018, or as soon as possible thereafter. The studentship will be based at the School of Electronic Engineering and Computer Science at QMUL and will be supervised by Prof Edmund Burke and Dr Jun Chen.

The studentship will investigate applications for network design, e.g. a town’s fibre network. The goal of the project is to explore software solutions to find optimal solutions to a graph problem described in a human readable declarative constraint language. It has two parts.

  • Design a declarative language for representing constraints on a graph, and the costs of the graph.
  • Implement a heuristic search engine which combines heuristic search techniques with constraint satisfaction methods to limit the search space. It is expected that the students will work in close collaboration with other researchers, both in QMUL and BT University Research.

Candidates should have a first class honours degree or equivalent (and preferably a Masters Degree) in Computer Science or a related field, and the ability to demonstrate mathematical and analytical skills. Strong programming skills and background in heuristics, metaheuristics, constraint handling and graph theory are also desirable. Successful applicants should have a clear commitment to research and should enjoy working at the intersection of theory and practice.

This studentship, funded by an EPSRC ICase, is for tuition fees plus a tax-free stipend starting at approximately £16.5K per annum. It 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 Dr Jun Chen.

To apply please follow the on-line process (see 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 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:

The closing date for applications is 16 March 2018.      

Interviews are expected to take place after 30 March 2018.

 

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PhD Studentship in Machine Learning for Automated Software Engineering


Application closing date: early application advised
Start date: September 2018 
Research group: Operational Research

Duration: 3 years
Funding available

The Operational Research Group 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.

The DAASE 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.

The successful candidate will pursue a course of research investigating the application of computational search methods, to software engineering challenges with a focus on real-world applications. These techniques could include machine learning approaches, big data techniques, and more broadly any metaheuristic, or operational research technique.

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 September 2018, however an earlier start date is possible for excellent candidates. The studentship is for three years, and covers international 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, Operational 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 optimisation techniques, heuristic and hyper-heuristic search is highly desirable although not mandatory.

Informal enquiries can be made by email to Dr. John Drake and Dr. John Woodward 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/how-to-apply/

The post will be held open until it is filled, so early application is highly advisable.

 

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


Application closing date: 18/03/2018 
Start date: May-June 2018 
Research group: Antennas and Electromagnetics Group

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.

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.

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 Physic
  • 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 , 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 instructions on the . At the page, select ‘Electronic 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, e.g., a chapter of your final year dissertation, or a published paper. More details can be found at:

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 http://eecs.qmul.ac.uk/profiles/dubrovkarostyslav.html.

Applicants seeking further information or feedback on their suitability are encouraged to contact Dr. Rostyslav Dubrovka 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 March 18, 2018.
Interviews are expected to take place in the end of March, 2018.
Starting date: May-June 2018 (dates can be flexible).

 

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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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