Research Fellow in Digital Manufacturing

Reference: B04-03739
Type: Contract

About the role

The post-holder will undertake research of significant industrial and academic relevance in the development of a software platform for the automation and optimisation of flow systems in autonomous chemical synthesis. In this EPSRC project the goal is to develop a digital twin platform where physics-informed machine learning (ML) and optimal experimental design algorithms are coupled to enable agile exploration of the chemical process design space, fast identification of optimal reaction conditions and mechanistic understanding of chemical reaction systems using the minimum number of experimental runs.

To achieve these goals, techniques for continuous flow processing, online advanced process analytics, optimal design of experiments, physics-informed ML and advanced data analysis need to be integrated to deliver a fully automated, self-optimizing platform for developing and manufacturing chemicals. The digital twin platform will enable to speed up laboratory research and accelerate process development in the chemicals and pharmaceutical manufacturing industries, facilitating the cost-effective continuous manufacture of chemical products.

This is a fixed-term role for 2 years in the first instance.

Salary is fixed to £39,508 per annum at grade 7 spine point 29. Appointment at Grade 7 is dependent upon having been awarded a PhD; if this is not the case, initial appointment will be at Grade 6B (salary £35,702 - £37,548 per annum) with payment at Grade 7 being backdated to the date of final submission of the PhD Thesis.

A job description and person specification can be accessed at the bottom of this page.

About you

The ideal candidate should have completed or near completed a PhD in Chemical Engineering, Computer Science or a related discipline, and have a proven track record of publications in the area of modelling, programming and simulation.The candidate should be able to demonstrate: - Knowledge of model validation techniques- Knowledge of data-driven modelling- Understanding of automation concepts- Understanding of first principles modelling- Knowledge of design of experiments techniques- Proficiency in mathematical modelling, optimisation and/or machine learning Knowledge of Python programming