PhD Student, Model Engineering

Role overview Turn research models into runnable, reproducible computational workflows that researchers can inspect and adapt. The work spans biological modeling approaches, from machine learning and mechanistic models to ordinary differential equation and stochastic simulations.

Responsibilities - Package research models as runnable labs and workflows. - Build workflows around public and newly published models so others can reproduce and modify them. - Write Python adapters and validation scripts to make models usable beyond demonstrations. - Prepare metadata, examples, and documentation for each packaged model.

Requirements - Current PhD student or PhD-track researcher in a relevant field, such as computational biology, genomics, systems biology, bioinformatics, or a related discipline. - Strong Python skills and comfort reading scientific papers, repositories, notebooks, and code. - Familiarity with biological modeling, machine learning systems, simulation workflows, or reproducible research. - Ability to make scientific workflows understandable and usable by others.

Nice to have Experience with standards or tools such as SBML, CellML, ONNX, PyTorch, JAX, SciPy, or workflow automation. The role is remote, part-time, and flexible.