Hycomplate
ML Research Engineer
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About Hycomplate
Hycomplate Ltd is a UK advanced-materials company developing conductive thermoplastic composites for fuel cell, electrolyser and flow battery hardware.
About the project
PROPEL-AI is a three-month Innovate UK-funded feasibility project, running October to December 2026. It asks whether uncertainty-aware machine learning can make materials design decisions more reliable. We are building calibrated surrogate models and an inverse-design workflow for composite formulation, and testing the model's predictions against independently measured materials.
The role
A short, well-defined implementation contract. You will work alongside the Project Lead to a clear technical specification, rather than setting research direction yourself.
What you will do
- Build Gaussian process surrogates with calibrated predictive uncertainty
- Run source-grouped validation and assess calibration and interval coverage
- Implement constrained multi-objective Bayesian optimisation
- Support structuring of a scientific literature dataset
- Deliver documented, reproducible code with a clean handover
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What you will bring
- Hands-on experience with Gaussian process regression and Bayesian optimisation in Python
- Working knowledge of PyTorch and GPyTorch, BoTorch, Pyro or similar
- A genuine understanding of uncertainty quantification and model calibration
- The ability to work independently against a specification Exposure to materials science, chemistry or engineering is a plus but not essential.
Well suited to a final-year PhD candidate, postdoctoral researcher or recent graduate who wants a paid, self-contained industrial project with a defined output.
Terms
- Fixed-price contract for services: £5,000 for approximately 13 days of work
- Worked flexibly between October and December 2026; start subject to funder confirmation, expected October
- Remote within the UK
- Contractors invoice through their own limited company or as a registered self-employed individual
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Eligibility
This work is funded by an Innovate UK grant and must be carried out in the United Kingdom. Applicants must be based in the UK, perform the work in the UK and hold the right to work here.
How to apply and hiring process
Apply through LinkedIn with your CV and a short note on your Gaussian process or Bayesian optimisation experience, plus links to any public code. Applications are reviewed on a rolling basis. Shortlisted candidates will be invited to a 30-minute technical conversation, and we aim to appoint in early October.
Applicant privacy
Application data is used only for this recruitment and is retained as part of the procurement records for the funded project.
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