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Postdoc Position in Machine Learning for Multiphysics Problems

Applicants are invited to apply for a postdoc position in data mining at University of Cambridge. The funds for this fellowship are available for two years. Applicants should apply before November 16, 2018.

A position exists, for a Research Associate in the Department of Engineering, to work on machine learning for complex problems with Multiphysics. Various machine learning algorithms will be explored for their applicability for turbulent combustion problems, with a specific long term aim of building a digital twin to test novel combustion concepts and technologies. This is a 2 year postdoctoral position with one year at Cambridge and another year at the Universit Libre de Bruxelles (ULB) and so a close collaboration between these two institutions is planned.

The post holder will be located for the first year in Central Cambridge, Cambridgeshire, UK.

The key responsibilities and duties are to (i) analyse very large numerical and experimental data using machine learning algorithms, (ii) explore building low-order models using the knowledge resulting from the data mining in (i), (iii) develop necessary computer codes required for the above two tasks, (iv) actively participate in the project discussion among the team members, and (v) prepare manuscripts and reports. Travel between Cambridge and Brussels is also involved.

The skills, qualifications and experience required to perform the role are (i) a fully completed and awarded PhD in the area of computational combustion, (ii) knowledge and experiences in analysis large data sets using clever and novel algorithms, (iii) ability to write computer codes using python, c/c++ or Fortran, (iv) ability to clearly communicate and disseminate the research findings through conferences and journal publications.

Fixed-term: The funds for this post are available for 12 months in the first instance.

Once an offer of employment has been accepted, the successful candidate will be required to undergo a health assessment.

Please ensure that you upload your Curriculum Vitae (CV) and a covering letter, publication list and, names and contact details of two referees who are familiar with your work in the Upload section of the online application. If you upload any additional documents which have not been requested, we will not be able to consider these as part of your application. Please submit your application by midnight on the closing date.

If you have any questions about this vacancy or the application process, please contact: Mrs Kate Graham, email: kag1000@cam.ac.uk (Tel. +44 01223 766805).


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