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

ETH Zurich; Basel; On-site; CHF 54,000 - 58,000; Full time

Benefits offered by this job

Conference travel

Training opportunities

Excellent computing resources

Job summary

ETH Zurich’s Computational Biology Group seeks a PhD candidate to develop the algorithmic infrastructure behind digital twins of biological systems. You will combine parameter estimation, uncertainty quantification and ML to advance image-based modelling and simulations.

You’ll work with modellers, experimentalists and clinicians, publish results, and contribute to open-source software and teaching within an international environment.

Qualifications

• A Master's degree in a quantitative field is required.
• Strong programming skills and clean, well-tested code.
• Experience with numerical methods, inference or ML.

Responsibilities

• Develop and benchmark parameter estimation and uncertainty quantification methods.
• Build and extend the simulation environment for performance and scalability.
• Apply methods to real models and datasets with collaborators.
• Publish results and present at international conferences.
• Contribute to open-source software and teaching.

Skills

Programming

Numerical methods

Machine learning

Python

C++

Education

Master's degree in Computer Science

Master's in Computational Science and Engineering

Master's in Applied Mathematics/Physics

Tools

Python

Julia

C++

PyTorch

JAX

Job description

The Computational Biology Group (Prof Dagmar Iber) at D-BSSE develops digital twins of embryonic developmentand reproductive health using image-based mechanistic modelling and simulation.

Project background

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.

Job description

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

Profile

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.

You should have:

• 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 biomedicineand 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.

We offer

• A 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 your scientific development, including conference travel and training
• Competitive ETH doctoral salary and conditions

Working, teaching and research at ETH Zurich

We value diversity and sustainability

In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish. Sustainability is a core value for us – we are consistently working towards a climate-neutral future.

About ETH Zürich

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