PhD Student, Model Engineering

What you will do

• Package AI/ML, mechanistic, ODE, stochastic, and standards-based biology models as runnable Biosimulant Hub labs.
• Build clear workflows around public and newly published models so researchers can reproduce, inspect, and adapt them.
• Write Python adapters, validation scripts, metadata, examples, and documentation that make each model usable beyond a one-off demo.

What you bring

• Current PhD student or recent PhD-track researcher in computational biology, genomics, systems biology, bioinformatics, ML for biology, pharmacometrics/QSP, synthetic biology, neuroscience, molecular modeling, or a related biotech field.
• Strong Python ability and comfort reading papers, model repositories, notebooks, and scientific code.
• Familiarity with biological modeling, ML systems, simulation workflows, or reproducible computational research.

Helpful background

• Experience with SBML, CellML, ONNX, PyTorch, JAX, SciPy, or workflow tooling.
• Taste for making complex scientific work understandable through examples, manifests, and runnable packages.

How to apply

Send a short note about your research area, links to code or papers if available, and one model or workflow you would want to make runnable in Biosimulant.