David Ginsbourger

This fully funded PhD position at the University of Berne offers a unique opportunity to work at the intersection of statistical machine learning, advanced microscopy, and live-cell biology. The project is a joint collaboration between the Pertz Lab (Institute of Cell Biology) and the Ginsbourger Group (Institute of Mathematical Statistics & Actuarial Science), providing a rich interdisciplinary environment for research and training. The successful candidate will be co-supervised by Prof. Olivier Pertz (Cell Biology) and Prof. David Ginsbourger (Statistics), and will be equally embedded in both research groups. The project aims to develop autonomous “self-driving” microscopes capable of building statistical models of biological dynamics in real time, predicting the most informative next experiment, and executing it automatically on living cells. This involves cutting-edge methods such as Gaussian Processes (heteroscedastic & multivariate), operator-valued and deep kernels, active Bayesian experimental design, physics-informed neural networks, and closed-loop control of biological systems. The Pertz Lab has pioneered optogenetic tools and fluorescent biosensors that enable direct perturbation and measurement of cellular signaling networks using light. By integrating these experimental capabilities with advanced statistical and machine learning techniques from the Ginsbourger Group, the project seeks to revolutionize experimental design and data-driven biological discovery. Applicants should have a Master’s degree or equivalent, strong skills in statistical modeling and probability theory, and experience in coding (Python, R, Julia, Matlab, etc.). Motivation to collaborate with experimental researchers and curiosity about biological systems are essential; prior wet-lab experience is not required. Good English language skills and 1–4 years of research experience in biological sciences are preferred. The position includes a small teaching duty in Statistics, which comes with an enhanced salary compared to standard PhD funding in Switzerland. The University of Berne is a top-ranked Swiss research university with an international community, high quality of life, and outstanding support for early-career researchers. Applications are reviewed on a rolling basis until the position is filled. For more details and to apply, visit the official job posting or contact the supervisors directly.

University of Bern is advertising a postdoc position in Statistical Data Science and Machine Learning for Climate Change Research within the Institute of Mathematical Statistics and Actuarial Science and the Oeschger Centre for Climate Change Research (OCCR) in Bern, Switzerland. The project is led by Prof. Dr. David Ginsbourger and Prof. Dr. Olivia Romppainen-Martius . The research focus includes generative modelling , operator learning , amortized inference , and the use of scientific machine learning for climate change research. Example application areas mentioned are climatology , meteorology , hydrology , and societal aspects of climate change. The recruited postdoc will also contribute to platform coordination and scientific animation activities, including support for graduate students and early career researchers, and may engage with other OCCR groups. The position is a 80-100% employment for 2 years, with a possible 1-year extension. Salary and employment conditions follow University of Bern standards. Eligibility highlights: candidates should have a PhD in statistics , machine learning , or applied mathematics , or alternatively a PhD in climate/atmospheric science , meteorology , or a related field with an excellent background in statistics and ML. Strong programming and data handling skills and proficiency in English are required. How to apply: submit a single PDF with CV, motivation letter, a short proposal (max 4 pages) describing your methodological research directions and how they connect to OCCR groups, plus contact details for 3 references. Send the application to [email protected] . Applications are reviewed on a rolling basis until the position is filled.