The Translational Organ Microsystems (TOM) Research Group at Julius-Maximilians-Universität Würzburg develops advanced in vitro models using biofabrication, biomaterials, and organ-on-chip technologies. Our interdisciplinary research combines engineering, biology, and computational approaches to better understand human physiology and disease.

The Master's thesis focuses on applying machine learning and computational modelling to microfluidic and organ-on-chip systems.

Your tasks will include:

• Developing computational models and simulations of microfluidic systems.

• Generating and analysing synthetic datasets.

• Implementing machine learning algorithms to predict system behaviour.

• Evaluating and validating model predictions using available data.

The project is entirely computational and does not involve wet-lab experiments.

• Currently enrolled in a Master's programme in Computer Science, Data Science, Artificial Intelligence, Computational Engineering, or a related field.

• Good programming skills in Python.

• Good knowledge of machine learning and data analysis.

• Interest in computational modelling, simulation (Comsol)

• Ability to work independently and good English communication skills.

Desirable but not essential:

• Experience with machine learning frameworks (e.g., PyTorch, TensorFlow, or scikit-learn).

• Familiarity with numerical simulations, fluid dynamics, or synthetic data generation.

Prior experience in biology or organ-on-chip technologies is not required.