Sort by
Refine Your Search
-
Listed
-
Program
-
Employer
- Technical University of Munich
- Forschungszentrum Jülich
- Heidelberg University
- Leibniz
- DAAD
- Nature Careers
- University of Oldenburg
- Academic Europe
- Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung
- Free University of Berlin
- GFZ Helmholtz-Zentrum für Geoforschung
- Lehrstuhl für Nachhaltige Thermoprozesstechnik und Institut für Industrieofenbau und Wärmetechnik
- Max Planck Institute for the Physics of Complex Systems
- University of Greifswald
- University of Tübingen
- Universität Freiburg, Historisches Seminar
- Universität Tübingen
- 7 more »
- « less
-
Field
-
qualifications: PhD or equivalent achievement (proof of independent research capability) in Machine Learning, Computer Science, Physics, Mathematics, or a related field Deep theoretical knowledge and extensive
-
are being developed that provide AI-supported tools to identify suitable sources and optimize utilization decisions throughout the product life cycle. Various machine learning approaches are to be used
-
-scale research facilities (e.g. DESY, ESRF), including coordination and setup of experiments Development of data workflows and analysis strategies (in collaboration with our machine learning team
-
European research consortia such as the DAPHNE (DAta for PHoton and Neutron Experiments) NFDI consortium and the Cluster of Excellence "Machine Learning: New Perspectives for Science". Details
-
to advancing machine learning in biomedicine. The Program focuses on developing and applying cutting-edge AI approaches to address key challenges in molecular biology, clinical research, and translational
-
twin of sperm motility, and utilize it to develop a separation method. Your tasks will include: Performing computer simulations and matching them to experimental data Very close collaboration with
-
Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Oldenburg Oldenburg, Niedersachsen | Germany | about 2 months ago
and strategies. We recently developed machine learning tools to recover plasmids from metagenomic assemblies and characterized their ecology and evolution in the human gut (https://www.nature.com
-
-party research funding are expected. We are particularly interested in a candidate in any field of economics who leverages state-of-the-art machine learning and causal inference methods to innovative
-
to the position must hold a doctoral degree in social or behavioral sciences (incl. human-computer interactions with relevant experience). Applicants must demonstrate experience in experimental work with human
-
and others) Analysis of the experimental data, ideally connecting to our machine learning tools Presentation of scientific results on conferences and in publications Requirements PhD degree in physics