Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Nature Careers
- Technical University of Munich
- Leibniz
- Forschungszentrum Jülich
- University of Tübingen
- Heidelberg University
- Free University of Berlin
- Fritz Haber Institute of the Max Planck Society, Berlin
- Max Planck Institute for Multidisciplinary Sciences, Göttingen
- WIAS Berlin
- ; University of Copenhagen
- DAAD
- Helmholtz-Zentrum Geesthacht
- Max Planck Institute for Heart and Lung Research, Bad Nauheim
- Max Planck Institute for Biology Tübingen, Tübingen
- Max Planck Institute for Molecular Biomedicine, Münster
- Max Planck Institute for Plasma Physics (Garching), Garching
- Max Planck Institute for Sustainable Materials GmbH, Düsseldorf
- Max Planck Institute for the Structure and Dynamics of Matter, Hamburg
- Max Planck Institute of Biochemistry, Martinsried
- Technische Universität München
- University of Greifswald
- 12 more »
- « less
-
Field
-
relevant to the Institute's research; experience with quantitative research methods and statistical analysis, ability to work independently and in interdisciplinary teams, with excellent organizational and
-
/ research stays, and/or presentations at international conferences; Proven experience in quantitative and/or qualitative social-science methods; Willingness and ability to further PRIF’s research agenda
-
, methods, and algorithms into existing high-performance frameworks, the fast prototyping of new ideas in individual code, an interest in the entire simulation pipeline: starting from simple algorithms
-
, Computational Biology, Applied Mathematics or related field Interest in interdisciplinary research Applied experience with machine/deep learning methods Ability to handle multiple projects in a dynamic
-
population health issues. Methods used in the group’s work include quasi-experimental techniques, descriptive epidemiology, and randomized trials. The fellow will be expected to publish in high-impact peer
-
us We are TUM’s unique Pathology AI lab developing new machine learning (ML) methods for automatically analyzing digital pathology data and related medical data. Such methods include the automatic
-
. Prerequisites Doctoral degree with quantitative training or research experience Training and experience in quasi-experimental methods is a plus Strong coding skills in R, Stata, or other statistical software
-
) and programming languages (e.g. Python, Matlab, R) as well as in advanced statistical methods for analyzing complex ecosystem and environmental datasets. Good knowledge of European marine ecosystems as
-
the team of Prof. Dr. Loriana Pelizzon on data and methods for supervision of climate and ESG risks in Capital Markets (incl. investment funds, bonds, stocks). The role involves advising and
-
research questions a strong collaborative spirit and enjoyment working closely within a diverse research team intellectual curiosity, creativity, and an openness to exploring new methods and