Postdoctoral Research Associate, Department of Music
Department of Music
Grade 7: - £33,797 to £40,322
Fixed Term - Full Time
Contract Duration: 9 months
Contracted Hours per Week: 35
Closing Date: 06-Nov-2020, 7:59:00 AM
The closing date for applications for this post is 5 November 2020 at 11.59 p.m
Durham University is one of the world's top universities with strengths across the Arts and Humanities, Sciences and Social Sciences. We are home to some of the most talented scholars and researchers from around the world who are tackling global issues and making a difference to people's lives.
The University sits in a beautiful historic city where it shares ownership of a UNESCO World Heritage Site with Durham Cathedral, the greatest Romanesque building in Western Europe. A collegiate University, Durham recruits outstanding students from across the world and offers an unmatched wider student experience.
Less than 3 hours north of London, and an hour and a half south of Edinburgh, County Durham is a region steeped in history and natural beauty. The Durham Dales, including the North Pennines Area of Outstanding Natural Beauty, are home to breathtaking scenery and attractions. Durham offers an excellent choice of city, suburban and rural residential locations. The University provides a range of benefits including pension and childcare benefits and the University’s Relocation Manager can assist with potential schooling requirements.
Durham University seeks to promote and maintain an inclusive and supportive environment for work and study that assists all members of our University community to reach their full potential. Diversity brings strength and we welcome applications from across the international, national and regional communities that we work with and serve.
Durham’s Department of Music is one of the very best in the UK, and indeed worldwide, with an outstanding reputation for excellence in research and teaching. Ranked third overall amongst music departments in the last national research evaluation (REF2014), we attract exceptionally talented undergraduate, MA and doctoral students, and focus on high-quality research-led teaching. We have a thriving and supportive research culture, with funding from national and international research councils and foundations and a community of postdoctoral fellows in the Department. In student-facing guides and league tables we are regularly ranked as the number one Music Department in the UK (Complete University Guide 2018, 2019, and 2020; The Sunday Times 2017, 2018 and 2019; Guardian 2019).
We are committed to equality, diversity and inclusion and are committed to increasing the diversity of our staff and student bodies as well as our curriculum, as evidenced by our recent application towards the Athena SWAN Bronze Award (see https://www.dur.ac.uk/music/edi/).
We have internationally renowned research strengths in musicology, theory and analysis, music technology and computational musicology, music psychology, ethnomusicology, composition, and performance. Our strategic priorities include nurturing innovative connections and interdisciplinary dialogue between these areas and enhancing the diversity and inclusivity of our research base. The research environment features world-class facilities, wide methodological expertise, opportunities for discussion and feedback, mentoring and other support schemes. We also benefit from University policies which support staff work-life balance and needs, for example by offering additional research leave following a period of parental leave.
The role is to work alongside Profs Martin Clayton and Tuomas Eerola as part of the EU Future and Emerging Technologies project ‘EnTimeMent - ENtrainment and synchronization at multiple TIME scales in the MENTal foundations of expressive gesture’. More details about the project can be found here: https://entimement.dibris.unige.it/.
Specifically, the research to be carried out in Durham, in collaboration with other partners including the University of Genoa and EuroMov (University of Montpellier), concerns analysis of the movement of musicians, performing solo, in duets and small groups: in the first instance these will be performers of North Indian (Hindustani) raga music. Using existing audiovisual recordings (see e.g. https://osf.io/nkjgz/) we will use pose estimation software to extract estimates of the movement of musicians (upper-body in the case of the Indian musicians). Audiovisual data will be pre-labelled with details of performers, repertory items and important musical events. Movement data extracted in this way, combined as appropriate with audio data, will be analysed to explore, for example, the components of movement at different time-scales and whether factors such as the musician’s identity, repertory item or performance stage can be predicted from movement data; and to explore the effects of interpersonal interaction on movement patterns.
We anticipate using a variety of data analysis techniques in this work, including machine learning. The post holder should be competent in the specific challenges encountered in analysing temporal data.
The post-holder will work with the Durham investigators and those at partner institutions (e.g. Prof. Benoit Bardy, Prof. Antonio Camurri). If possible they will be based at the Music Department and have access to facilities including the Music and Science Lab. It is possible that for all or part of the period it may be necessary to work remotely, and for the University to provide access to the required facilities (including our GPU cluster if necessary). We will be flexible, therefore, in negotiating the place of work. Visits to partner institutions may be possible, depending on international travel restrictions.
We will be happy to consider job share or part-time working arrangements.
- Work as a member of the EnTimeMent project team to advance specific aspects of the research programme, in particular they will work with body movement data extracted with OpenPose to:
- Characterize movement patterns of musicians in different conditions and at different time-scales.
- Explore the use of machine learning techniques and libraries to predict the performers’ identity or repertoire item from movement data, or to predict audio features from movement data.
- Document and share details of data pipelines, including any new elements of code developed as part of the project.
- To understand and convey material of a specialist or highly technical nature to the team or group of people, contributing to writing up, presenting and publishing results.
- To prepare and deliver presentations on research outputs/activities to audiences which may include: research sponsors, academic and non-academic audiences.
- To contribute to the publishing of high quality outputs (leading on at least one), including papers for submission to peer reviewed journals and papers for presentation at conferences and workshops under the direction of the Grant-holder.
- To assist with the development of research objectives and proposals.
- To conduct research under the direction of the Grant-holder.
- To deal with problems that may affect the achievement of research objectives and deadlines by discussing with the Grant-holder and offering creative or innovative solutions.
- To liaise with research colleagues and make internal and external contacts to develop knowledge and understanding to form relationships for future research collaboration.
- To plan and manage their own research activity in collaboration with others and contribute to the planning of the research project.
- To deliver training in research techniques/approaches to peers, visitors and students as appropriate.
- To contribute to fostering a collegial and respectful working environment which is inclusive and welcoming and where everyone is treated fairly with dignity and respect.
- To engage in wider citizenship to support the department and wider discipline.
- To engage in continuing professional development by participation in the undergraduate or postgraduate teaching programmes or by membership of departmental committees, etc. and by attending relevant training and development courses.
This post is fixed term for 9 months due to external funding. It is anticipated that the successful candidate will be in post by 1 December 2020, or sooner if possible.
The post-holder is employed to work on research/a research project which will be led by another colleague. Whilst this means that the post-holder will not be carrying out independent research in his/her own right, the expectation is that they will contribute to the advancement of the project, through the development of their own research ideas/adaptation and development of research protocols.
How to Apply
For informal enquiries please contact Prof. Martin Clayton at email@example.com. All enquiries will be treated in the strictest confidence.
We prefer to receive applications online via the Durham University Vacancies Site. https://www.dur.ac.uk/jobs/. As part of the application process, you should provide details of 3 (preferably academic/research) referees and the details of your current line manager so that we may seek an employment reference.
Applications are particularly welcome from women and black and minority ethnic candidates, who are under-represented in academic posts in the University.
What to Submit
All applicants are asked to submit:
- A CV and covering letter which details your experience, strengths and potential in the requirements set out above;
- Up to two published papers on relevant topics to which you have contributed.
The assessment for the post will include a 20 minute research presentation. Shortlisted candidates will be invited for interview and assessment as soon as possible following the closing date.
A PhD in a relevant area: for example, machine learning, data science, integrative neurosciences, human movement science, experimental psychology.
- Experience of data analysis using Python, R or another scripting language, and the ability to quickly acquire additional programming skills as needed
- Experience of machine learning techniques and understanding of their application to temporal/ time-series data
- Experience with PyTorch or TensorFlow/Keras
- Experience with supervised deep learning methods.
- Demonstrable ability to work cooperatively as part of a team, including participating in research meetings.
- Ability to work independently on own initiative and to strict deadlines.
- Excellent interpersonal and communication skills.
- Experience developing audio and visual classification models using deep learning
- Experience working with human movement data, including pose estimation software
- Experience working in a multidisciplinary research team
- Knowledge and experience of music performance research
- Knowledge of or interest in Indian music
- Demonstrable ability to plan and manage independent research.
DBS Requirement: Not Applicable.