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The Computational Biology Group at D-BSSE develops digital twins of embryonic development and reproductive health using image-based mechanistic modelling and simulation. Digital twins of living systems are a challenging computational problem, involving nonlinear, high-dimensional models with uncertain parameters fitted to noisy data, and simulations requiring new algorithms and scalable software. The group applies these methods to questions such as tissue self-organisation during embryonic development and digital twins supporting IVF treatment planning.

Tasks

  • Develop and benchmark methods for parameter estimation and uncertainty quantification, including classical optimisation, Bayesian inference, physics-informed neural networks (PINNs), and other machine-learning approaches.
  • Build and extend the simulation environment, focusing on performance, scalability, and reproducibility.
  • Apply methods to real models and data, including imaging-based models of tissue development and digital twins of the hormonal cycle and IVF treatment, in collaboration with modellers, experimentalists, and clinical partners.
  • Publish results and present at international conferences.
  • Contribute to the group's open-source software and to teaching.

Requirements

  • Master's degree in Computer Science, Computational Science and Engineering, Applied Mathematics, Physics, or a related field.
  • Strong programming skills, e.g. in Python, Julia, or C++, with experience in writing clean, well-tested code.
  • Experience in numerical methods, optimisation, statistical inference, or machine learning, gained through coursework, thesis, or project.
  • Interest in biology and biomedicine and willingness to learn the biological background of the project; prior knowledge of biology is not required.
  • Good written and spoken English.
  • Experience with PDE-based simulations, finite element methods, high-performance computing, or deep-learning frameworks (e.g. PyTorch, JAX) is an advantage.

Benefits

  • PhD position in an interdisciplinary, international research environment at the interface of mathematics, computing, and biology.
  • Close collaboration with leading experimental groups and clinical partners.
  • Excellent infrastructure and computing resources at ETH Zurich.
  • Support for scientific development, including conference travel and training.
  • Competitive ETH doctoral salary and conditions.
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Über uns
Die ETH Zürich ist eine technisch-naturwissenschaftliche Hochschule des Bundes mit Sitz in Zürich. Wir betreiben Lehre, Forschung und Wissenstransfer in Bereichen wie Ingenieurwissenschaften, Naturwissenschaften, Architektur, Mathematik, Informatik, Management sowie Sozial- und Geisteswissenschaften. Unsere Ausbildung umfasst Bachelor-, Master-, Doktorats- und Weiterbildungsangebote und ist eng mit internationaler Spitzenforschung verbunden. Als Teil des ETH-Bereichs entwickeln wir wissenschaftliche Grundlagen, Technologien und Lösungen für Gesellschaft, Wirtschaft und globale Herausforderungen.
Das Team

The Computational Biology Group is part of the Department of Biosystems Science and Engineering (D-BSSE) and collaborates closely with experimental groups and clinical partners.

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