PhD Position in Computational Methods for Digital Twins of Embryonic Development & Disease

The PhD position focuses on developing the algorithmic infrastructure behind our digital twins. You will:

• develop and benchmark methods for parameter estimation and uncertainty quantification, from classical optimisation and Bayesian inference to physics-informed neural networks (PINNs) and other machine-learning approaches
• build and extend our simulation environment, with a focus on performance, scalability and reproducibility
• apply your methods to real models and data, from imaging-based models of tissue development to our digital twin of the hormonal cycle and IVF treatment, together with the modellers, experimentalists and clinical collaborators in the group
• publish your results and present them at international conferences
• contribute to the group's open-source software and to teaching

The Computational Biology Group (Prof Dagmar Iber) 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 hard computational problem: nonlinear, high-dimensional models with uncertain parameters that must be fitted to noisy data, and simulations so expensive that fitting them demands new algorithms and scalable software. We apply this to real questions, from how tissues self-organise during embryonic development to digital twins that support IVF treatment planning. You will build the algorithmic infrastructure that makes this possible, working with modellers, experimentalists and clinicians, and building on our open-source simulation tools.

ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.

• We are looking for a self-motivated candidate who is enthusiastic about developing algorithms and software for data-based modelling of biological systems. The position suits someone with training in computer science, scientific computing or a related quantitative field, and an interest in applying it to biology and biomedicine.

• a 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++, and experience in writing clean, well-tested code

• experience in numerical methods, optimisation, statistical inference or machine learning, gained through coursework, a thesis or a project

• an interest in biology and biomedicine and the 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.