Research Associate in Statistical Mechanics of Nonequilibrium Processes

The Department of Chemical Engineering invites applications by highly talented researchers for a Postdoctoral Research Associate position in Statistical Mechanics of Nonequilibrium Processes as part of the ERC Advanced Grand “Machine-Aided General Framework for Fluctuating Dynamic Density Functional Theory (MAGFFDDFT)”. 

The overarching objective is the development of a generic theoretical-numerical framework aided by machine learning that would allow us to obtain rationally and systematically optimal low-dimensional general laws governing the dynamics of observables, which in turn can be used for the accurate and efficient analysis of classical fluids, and complex multiscale systems (CMS) in general, far from equilibrium conditions. 

Your work will be primarily theoretical aiming at the development of new models from first principles and exploring the mathematical structure of the theory. But you will also work closely with the parallel computational effort on the project involving physics-informed machine learning (ML), in particular coupling the theoretical developments with ML.   

In the long term, the framework could potentially shift the paradigm in a number of fields, while targeted applications will ultimately establish it as a useful and versatile toolbox for the mathematical and computational scrutiny of CMS.

You will lead the following two main project themes: 

You will also disseminate the research findings with peer-reviewed publications and conference presentations, and you will assist the other researchers in the Group and the Principal Investigators.

You should have (or about to receive) a PhD in Applied Mathematics, Engineering, Physics, or other related field, and a strong foundation and proven record in mathematical techniques (analytical, numerical) and general mathematical modelling. Given the technical complexity of the research, you should have strong problem-solving skills. 

Experience or good knowledge with some or all of the following areas is highly desirable: applied analysis of PDEs/SPDEs, stochastic processes, mode reduction and coarse graining for SPDEs, and physics, especially fluid dynamics, statistical physics and quantum field theory.

The post offers a unique opportunity to be part of a world-class research environment at Imperial College. You will join a highly interdisciplinary team working on a wide variety of exciting projects at the interface between applied mathematics and engineering science fundamentals.

*Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant within the salary range £40,694 – £43,888 per annum.

You can start immediately, and the post is available for 36 months in the first instance, with the possibility of extension subject to research funding. The post is based in the Department of Chemical Engineering at Imperial College London (South Kensington Campus).  

Informal enquiries about the post can be made to Professor Serafim Kalliadasis ([email protected] ).

To apply, please click the Apply now button at the top of the page. Please ensure you include a completed application form with your submission.  

Further information about the post is available in the job description. 

Should you have any queries about the application process please contact [email protected] . 

Closing date: Midnight on Monday 12th February 2024

For technical issues when applying online please email [email protected]   

The College is currently trialling a Work Location Framework until early 2023.  Hybrid working may be considered for this role and the role holder may be expected to work 60% or more of their time onsite, with 40% the minimum time spent onsite.  The opportunity for hybrid working will be discussed at interview. 

The College is a proud signatory to the San-Francisco Declaration on Research Assessment (DORA),which means that in hiring and promotion decisions, we evaluate applicants on the quality of their work, not the journal impact factor where it is published. For more information, see https://www.imperial.ac.uk/research-and-innovation/about-imperial-research/research-evaluation/

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