PhD Position in Statistical Machine Learning for Self-Driving Microscopy
at the University of Bern, Switzerland
Your Environment
You will be part of two complementary research communities:
-
Pertz Lab -- Institute of Cell Biology (ICB)
A leading group pioneering real-time control of cellular signaling dynamics using microscopy, quantitative imaging, and optogenetics. This interdisciplinary team has close ties to engineering and data science.
-
Ginsbourger Group -- Institute of Mathematical Statistics and Actuarial Science (IMSV)
An internationally recognized group in Gaussian process modeling, Bayesian optimal design, and machine learning for scientific discovery. The group fosters strong collaborations across mathematics, machine learning, and applied sciences.
You will regularly move between both labs and become fluent in communicating across disciplines—a major asset for your career.
Project Overview
Cells sense, integrate, and respond to dynamic stimuli through complex signaling networks. The Pertz Lab has developed powerful optogenetic tools and fluorescent biosensors that allow direct perturbation and measurement of these networks using light. Together with the Ginsbourger Group, we aim to create autonomous "self-driving" microscopes that:
- Build statistical models of biological dynamics in real time
- Predict the most informative next experiment
- Execute it automatically on living cells
Key methods will include:
- Gaussian Processes (heteroscedastic & multivariate)
- Operator-valued and deep kernels
- Active Bayesian experimental design
- Physics-informed neural networks
- Closed-loop control of biological systems
This project offers a unique opportunity to see mathematical models come to life, guiding experiments as they happen. The position also involves a small teaching duty in Statistics, which includes an enhanced salary compared to standard PhD funding in Switzerland.
Why Choose Bern?
- Top-ranked Swiss research university
- International community with a high quality of life
- Easy access to Europe
- Outstanding support for early-career researchers
Your Profile
- Master's degree in Mathematics, Statistics, Computer Science, or Engineering
- Strong skills in statistical modeling & probability theory
- Experience in coding (Python, R, Julia, Matlab, etc.)
- Motivation to collaborate closely with experimental researchers
- Curiosity about biological systems—no prior wet-lab experience needed!
Application
Apply online using the form below. Only applications matching the job profile will be considered.