Research Associate - Machine Learning and Battery Degradation

University of Cambridge - Department of Physics

Fixed-term: The funds for this post are available for 36 months in the first instance.

Postdoctoral research associate: Machine learning and the physics of battery degradation

We invite applications for a Postdoctoral Research Associate position in the Department of Physics with Dr Alpha Lee's research group. The position is funded for up to 36 months by the EPSRC and this position can be taken up from 12th March 2018. This project is a part of the Faraday Challenge fast start project on battery degradation (a large consortium led by Professor Clare Grey between Cambridge, Glasgow, UCL, Imperial, Liverpool, Manchester, Newcastle, Southampton and Warwick with 25 investigators).

Dr Alpha Lee's group in the Department of Physics, University of Cambridge (www.alpha-lee.com) aims to design materials by combining physics with machine learning. Recent research highlights include developing methods inspired by statistical physics and random matrix theory to predict protein-ligand affinity, inferring probabilistic models using a liquid state theory approach, and deriving a scaling theory for the structure of battery and supercapacitor electrolytes.

The post holder will elucidate physical mechanisms of battery degradation by using machine learning algorithms to analyze large experimental datasets. Those data include current-voltage-time measurements, electrochemical impedance spectroscopy and other characterization techniques. Those datasets will be bespoke generated as part of the consortium. The project aims to arriving at a new paradigm for improving battery performance.

Candidates should have (or be about to obtain) a PhD in physical chemistry, physics, machine learning, applied mathematics or a closely related discipline. They should have a proven track record in research and publications in areas related to this project described above. Knowledge in machine learning and/or electrochemistry is an advantage but not required. Applicants will be expected to work in an interdisciplinary environment and interact with a team of scientists with a wide range of expertise in materials science and electrochemistry.

To apply online for this vacancy and to view further information about the role, please visit: http://www.jobs.cam.ac.uk/job/16206. This will take you to the role on the University’s Job Opportunities pages. There you will need to click on the 'Apply online' button and register an account with the University's Web Recruitment System (if you have not already) and log in before completing the online application form.

The applicant should upload their application by the closing date of 7th March 2018. The names and contact details of three referees are a necessary part of the submission. Referees will be contacted automatically following an application but applicants are strongly advised to inform nominated referees of the need to provide references by Wednesday, 7th March 2018.

Further information may be obtained from Dr Alpha Lee, by email: aal44@cam.ac.uk

Please quote reference KA14406 on your application and in any correspondence about this vacancy.

The University values diversity and is committed to equality of opportunity.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.

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