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Postdoctoral Research Associate in Cardiovascular Music Optimisation
Department :	Cardiovascular Imaging
Job description This is an exciting opportunity for a data scientist with experience in cardiovascular signal processing and interests in music to play a key role developing computational techniques to optimise music expressivity to achieve specific cardiovascular aims. The objectives will be to design and implement computational techniques to infer causal relationships between expressive music parameters and surface or intracardiac measurements; and, to re-model the music parameters to achieve targeted physiological responses. The remodelled musical expressions will be rendered through a reproducing piano. The effectiveness of the strategies will be validated through physiological measures. The work will be carried out in the context of the ERC project COSMOS (Computational Shaping and Modeling of Musical Structures, https://cosmos.isd.kcl.ac.uk ), assisted by tools created in COSMOS and in the Proof-of-Concept project HEART.FM (Maximizing the Therapeutic Potential of Music through Tailored Therapy with Physiological Feedback in Cardiovascular Disease, https://heartfm.kcl.ac.uk), on citizen/data science approaches to studying music expressivity and on autonomic modulation through music. Strategies for music re-shaping will be integrated into a web-based citizen science portal in collaboration with the CosmoNote ( https://cosmonote.isd.kcl.ac.uk) software engineer. The successful candidate will make major contributions to, and be involved in, all aspects of the computational modelling, algorithm design, and software development, testing, and validation, including on listeners (healthy volunteers or patients); liaising with research team members, and with collaborators across multiple domains, and be able to prioritise and organise their own work to deliver research results. The successful candidate will have a PhD in data science or a closely-related field, with experience in cardiovascular signal processing, and demonstrated facility with statistical and optimisation techniques. Having sound musical judgement is a plus. They should be highly motivated and keep abreast of research developments, particularly in cardiovascular science. They should have strong written and oral communication skills, and a good track record of scientific publication. Personal integrity, a strong work ethic, and a commitment to uphold the highest standards in research are essential attributes. The project is hosted by the Department of Cardiovascular Imaging in the School of Biomedical Engineering & Imaging Sciences (BMEIS) in the Faculty of Life Sciences & Medicine (FoLSM), and by the Department of Engineering in the Faculty of Natural, Mathematical & Engineering Sciences, at King’s College London. KCL was ranked 6th nationally in the recent Research Excellence Framework exercise. FoLSM was ranked 1st and Engineering was ranked 12th for quality of research. The research will take place in BMEIS, which is embedded in St Thomas’ Hospital, and Becket House, on the south bank of the River Thames, overlooking the Houses of Parliament and Big Ben in London. This post will be offered on a fixed-term contract until 31 May 2025 (or up to 30 Nov 2025, pending a 6-month no-cost extension approval) This is a full-time post
Key responsibilities Key responsibilities and outcomes Building models to infer causal relationships between expressive music parameters and surface or intra cardiac signals Designing and developing algorithms (and sandbox environments in collaboration with software engineer) to remodel musical expressivity for targeted cardiovascular outcomes Evaluating and validating the proposed methodologies and assessing their effectiveness and potential for clinical translation Following the principles of good software design, development, and documentation practices Preparing high-quality manuscripts for publication, writing clearly about the computational techniques, outcomes, and design analytics Presenting key findings at scientific conferences and public engagement events Maintaining suitable performance levels for the software, following good software design, development, and documentation practices General Demonstrate collaborative approach to research and software development Liaise directly with internal / external colleagues in an independent manner Use initiative, discretion, knowledge and experience in planning, coordination and problem-solving Demonstrate ownership of tasks and development of solutions to problems Governance Maintain an awareness and observation of ethical rules and legislation governing the storage of projected data Maintain an awareness and observation of confidentiality agreements with collaborators and external organisations Maintain an awareness and observation of appropriate procedures for the disclosure and protection of inventions and other intellectual property generated as part of the post holder’s activities and other team members working within the project Development To attend regular project meetings and training courses for professional and personal development as required Communication & Networking Develop and maintain effective working relationships with staff within the School as well as externally Regularly communicate information in a clear and precise way Decision Making, Planning & Problem Solving Lead in decisions that have a significant impact on their own work, that of others and be party to collaborative decisions Manage own workload, prioritising these in order to achieve their objectives Communicate to management any difficulties associated with carrying out work tasks The above list of responsibilities may not be exhaustive, and the post holder will be required to undertake such tasks and responsibilities as may reasonably be expected within the scope and grading of the post. Skills, knowledge, and experience Essential criteria 1. PhD in biomedical engineering, operations research and statistics, computer science, or a closely related field* 2. Experience analysing and processing cardiovascular signals, and willingness to learn about cardiovascular research developments 3. Experience with statistical analysis and optimization techniques 4. Ability to work with real-world music and physiological data 5. Knowledge of software design principles and code management on Git 6. Excellent written and oral communication skills 7. Track record of high-quality, peer-reviewed scientific publications 8. Ability to work with people from diverse backgrounds and specialties Desirable criteria 9. Experience analysing and processing music signals 10. Music performance experience, ability to judge quality of music expressivity Please note that this is a PhD level role but candidates who have submitted their thesis and are awaiting award of their PhDs will be considered. In these circumstances the appointment will be made at Grade 5, spine point 30 with the title of Research Assistant. Upon confirmation of the award of the PhD, the job title will become Research Associate and the salary will increase to Grade 6.
Grade and Salary :	£42,405 - £47,178 per annum, including London Weighting Allowance	Job ID :	073048
Post Date :	11-Aug-2023	Close Date :	09-Oct-2023
Contact Person :	Professor Elaine Chew	Contact Details :	elaine.chew@kcl.ac.uk

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