Research Associate (Fixed Term) in Machine Learning and Battery Degradation University of Cambridge, Department of Physics United Kingdom

Research Associate (Fixed Term) - Machine Learning and Battery Degradation

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


Postdoctoral research associate: Machine learning and 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 1st February 2018 or soon after. This project is a part of the fast start project on battery degradation within the Faraday Challenge (a large consortium between Cambridge, Glasgow, UCL, Imperial, Liverpool, Manchester, Newcastle, Southampton and Warwick with 25 investigators).


The post holder will design machine learning algorithms that can correlate real time measurements such as electrochemical impedance spectroscopy to independently measurable battery degradation mechanisms. The aim of the project is to arrive at data-driven models that can forecast degradation events, make mission-critical decisions on stopping a battery to avert catastrophic failure mode such as thermal runaways, as well as devising optimal battery usage strategies that can prolong the lifetime of batteries. Those machine-learning algorithms will fit into the design of novel battery management systems for applications such as electrical vehicles. The post holder will work in a large consortium where experimental data will be systematically generated for this project, and work with experimental teams to develop data management strategies for large data streams (e.g. cloud computing).


Candidates should have (or be about to obtain) a PhD in physical/theoretical chemistry, physics, engineering, machine learning or related disciplines. 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, please click on the 'Apply' button below. This will route you to the University's Web Recruitment System, where you will need to register an account (if you have not already) and log in before completing the online application form.


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 Sunday, 21st January 2018.


Further information may be obtained from Dr Alpha Lee, by email:


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.

Further information
Further Information

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