Our Team
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David S. Fischer
David is passionate about leveraging knowledge from molecular biology in machine learning models to extract more tangible insights from large snapshot datasets of cellular systems. He trained in Biochemistry at the University of Cambridge (B.A., UK), in Computational Biology and Bioinformatics at ETH Zurich and the University of Zurich (M.Sc., CH), and developed machine learning methods to analyze omics data at Helmholtz Munich and TU Munich (PhD, DE) and at the Broad Institute of Harvard and MIT (PostDoc, US). David is an assistant professor at the Medical University of Vienna (AT).
Join us!
We are excited to meet people who are motivated to improve mechanistic interpretability of machine learning models in biomedicine! If that’s you — contact us! We’ll try our best to help you, for example if you are looking for a temporary research project, including Master thesis projects, a PhD position, or a PostDoc position. We list open calls below, but we are also happy to discuss proactive or initiative applications!
In our work, we depend on teams. The interactions in these teams matter to us. Read about our values.
PhD Student and Postdoc Position
Developing Machine Learning Methods to Understand Mechanisms of Cell Biology
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We are recruiting a PhD student and a Postdoc who want to pursue a scientific career in machine learning method development for biomedicine. We also have open Master thesis projects. Our research focuses on (1) machine learning models of dynamical systems with mechanistic interpretation and feedback from experiments (lab-in-the-loop) and (2) applications in precision medicine and high-throughput biology (single-cell and spatial omics). The successful candidates will be based in the group of David Fischer at the Institute of Artificial Intelligence at the Medical University of Vienna, with ample opportunities to integrate into the national and international research landscape. We are looking for candidates with a background in computational sciences, machine learning, or dynamic systems, and an interest in cell biology or medicine. The pursuit of virtual cells and applied machine learning in biomedicine are gaining significant attention, and we are excited to build momentum around these positions by establishing a dedicated team to address this research focus and by building a network of biomedical collaboration partners.
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This position focuses on modeling dynamic systems, specifically inferring homeostatic and dynamic mechanisms from snapshot experiments common in cell biology, such as single-cell and spatial transcriptomics. We aim to build “virtual cells”: computational methods that allow us to simulate how cells in organisms change during disease and treatment. We are particularly excited about applying these machine learning methods to immuno-oncology, infectious disease, autoimmunity, and the engineering of cellular therapeutics. You will contribute creatively and proactively to the development of new mechanistic machine learning methods tailored to cutting-edge omics experiments and their application in biomedicine as part of interdisciplinary teams.
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We are seeking individuals who are interested in developing the next generation of mechanistic machine learning methods for biomedicine, with a research focus on reflecting dynamic systems in these models. We aim to overcome challenges in interpretability of black-box models through the usage of large-scale prior knowledge, for example in the shape of networks. Usage of these priors requires candidates to engage with the underlying molecular biology; however, previous experience in biology is not strictly necessary. We are looking for candidates with a background in machine learning, computer science, statistics, physics, bioinformatics, systems biology or similar fields. Candidates with a background in biology or medicine are also eligible if they possess strong quantitative skills and experience in machine learning.
Interested, but not a perfect fit for this specific position? Contact us to explore other opportunities for collaboration!
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Contact us!
Deadline: None
Master theses
and other temporary research projects
Various topics in Machine Learning methods and their applications to Cell Biology
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We invite Master students seeking Master thesis projects or other students seeking similar temporary research placements to discuss opportunities with us on a rolling basis! We can integrate students in a variety of ongoing projects. Read about our past publications to get an idea of the type of project you can expect to work on. Projects include the development of machine learning methods and/or their application to specific datasets and questions in Cell Biology. We will plan and adjust projects together with candidates to match their skill sets and desired learning outcomes.
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We are looking for candidates with a background in machine learning, computer science, statistics, physics, bioinformatics, systems biology or similar fields. Candidates with a background in biology or medicine are also eligible if they possess strong quantitative skills and experience in machine learning.
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Contact us!
Deadline: None.