Machine Learning Scientist (Closed)
Deadline: 2nd May 2023 (cannot guarantee applications to be considered afterwards)
You will be working closely together with customers in the materials space to build OpenSource machine learning models that will inform their experimentation schedule. Specifically, you will identify, implement and test models for biosynthetic models. Try our 5 minute tutorial to see what that looks like: https://matterhorn.studio
You work will focus on:
- Researching the right Bayesian Optimisation techniques for a variety of experimentation challenges: multi-fidelity, multi-source, multi-step (generally known as ‘grey-box’ methods, see https://arxiv.org/abs/2201.00272)
- Implementing these models as OpenSource python packages to be shared with the material science community, focusing on ease of usability and interpretation
- Validating the effectiveness of the models and tackling deeper research challenges, with the opportunity to publish.
You must have relevant experience in Machine Learning, specifically Bayesian Optimisation, at MSc/PhD level to complete the above work, exceptions can be made if you can show that you can pick up these skills in the first weeks of the job.
Location: Hybrid/Oxford/London
Length: In a first instance, limited to 6 months, from 1st of June to 30th of November, with an option for extension or permanent position.
Matterhorn Studio is a young company with a bold mission and we’re looking forward to see how we can shape the future of material science together with you.
Contact Jakob at causalminds@gmail.com with a CV and a few ideas of who you’d approach the above work.
Get in touch
We will happily guide you through the emerging space of data-driven material discovery. We look forward to learn from your experience and problem space.
