Senior Researcher - Physics Informed Machine Learning

1 day per week in the office outside of London - could be less if required

Up to £60,000 + bonus

MBN Solutions have been retained by our client who are well respected in the field of research to help them bring on board a Senior Researcher specialising in Physics Informed Machine Learning.

You will be working on exploring how to use Machine Learning in Scientific Computing related applications and we have a strong preference for the development of AI techniques that can be applied in the real world and that directly contributeto social good.

The Job:

• The development of new state-of-the-art of physics-informed machine learning techniques focused on atomistic simulation of molecules and materials
• Implementation of these techniques into proof-of-concept code and demonstrations that can be used to present our innovation to internal and external stakeholders and get their feedback
• Monitoring the wider area of physics-informed machine learning in order to be up to date with advances in other fields such as computational fluid dynamics or weather prediction
• Acquiring domain specific knowledge in target application areas, that will enable you to both communicate with people in that field and to develop AI methods that can contribute to it
• Generating IP and publishing work in top academic conferences and journals

The Person:

• Pro-active in your approach, adaptable and able to demonstrate that you have experience with applying physics-informed machine learning techniques for surrogate modelling for atomistic simulations, such as machine-learning force fields
• High proficiency in one or more programming languages(Python preferred) and experience with one or more Deep Learning framework (PyTorch preferred)
• A track record of writing academic publications
• Excellent written and verbal communication skills
• Experience with high-performance computing (HPC) or parallel computing
• Application of physics informed machine learning for other scientific computing fields such as computational fluid dynamics, structural analysis or weather modelling
• Experience with scientific computing and numerical methods for simulation