PhD Student and Postdoc Position, David Fischer Group, Vienna: Developing Machine Learning Methods to Understand Mechanisms of Cell Biology
Saturday 7th September 2024
Contact Email for the Job Posting [email protected]
Organization Medical University of Vienna, Center for Medical Data Science
Location Vienna, Austria
Title PhD Student and Postdoc Position, David Fischer Group, Vienna: Developing Machine Learning Methods to Understand Mechanisms of Cell Biology
URL https://static1.squarespace.com/static/66b3cd5797095702afffc18a/t/66daef7c20248657f047cd6d/1725624188996/202409_PhD_and_PostDoc_FischerLab.pdf
Closing date Oct 31, 2024
Description 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.
The candidate
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!
We are looking forward to hearing from you! If you want to be part of this interdisciplinary effort and change how we think about machine learning in biomedicine, send your application and any questions you might have to [email protected], ideally as a single PDF document. The application materials should include a cover letter, CV, academic transcripts, and contact details of up to 2-3 references whom you have previously worked with. The deadline is October 31st 2024. Start dates are flexible.