Welcome from the Computational Modelling Group

A picture showing several members of the CoMo Group

Welcome to the website of the CoMo Group. We develop and apply modern numerical methods to problems arising in Chemical Engineering. The overall aim is to shorten the development period from research bench to the industrial production stage by providing insight into the underlying physics and supporting the scale-up of processes to industrial level.

The group currently consists of 22 members from various backgrounds. We are keen to collaborate with people from both within industry and academia, so please get in touch if you think you have common interests.

The group's research divides naturally into two inter-related branches. The first of these is research into mathematical methods, which consists of the development of stochastic particle methods, computational fluid dynamics and quantum chemistry. The other branch consists of research into applications, using the methods we have developed in addition to well established techniques. The main application areas are reactive flow, combustion, engine modelling, extraction, nano particle synthesis and dynamics. This research is sponsored on various levels by the UK, EU, and industry.

Markus Kraft's Signature
Markus Kraft - Head of the CoMo Group

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PhD studentships available

17th May, 2018

Two fully-funded 3.5 year PhD studentships are available in the Computational Modelling Group, Department of Chemical Engineering and Biotechnology, at the University of Cambridge under the supervision of Prof. Markus Kraft. Both studentships start in October 2018 and, due to funding regulations, are only able to cover fees at the home/EU rate.

We are looking for outstanding students who are interested in working with us on any of the following areas:

  • To develop state-of-the-art computational tools to model the formation of titanium dioxide nanoparticles.
  • To perform laminar flame experiments to investigate the factors controlling the morphology of titanium dioxide nanoparticles.
  • To design and implement advanced algorithms, based on statistical and/or machine learning/artificial intelligence techniques, and apply them to a range of practical problems - as part of our software tools that perform a variety of tasks such as optimisation, data-driven modelling, uncertainty analysis, parameter estimation, surrogate generation, and experimental design for computationally expensive models.

For more information on these positions, and how to apply, please use the following links: