Sort by
Refine Your Search
-
Listed
-
Employer
- Fraunhofer-Gesellschaft
- Nature Careers
- Technical University of Munich
- Leibniz
- Free University of Berlin
- Forschungszentrum Jülich
- University of Tübingen
- DAAD
- Heidelberg University
- ;
- Max Planck Institute for Biology of Ageing, Cologne
- Max Planck Institute for Brain Research, Frankfurt am Main
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg
- Max Planck Institute of Molecular Cell Biology and Genetics
- RWTH Aachen University
- Technische Universität München
- 6 more »
- « less
-
Field
-
of interest include, but are not limited to: AI methods that meet the complexity of living systems, high-dimensional machine learning for biology, statistical machine learning, AI‑driven laboratory automation
-
in high-performance computing, materials chemistry, theoretical chemistry, molecular dynamics, data science, and machine learning are beneficial. What we offer: We offer a position with a competitive
-
or CNC machines You have already worked with Python or other data analysis software, or are motivated to acquire the relevant skills What you can expect 👥 Team spirit: Creative and interdisciplinary
-
, check your computer’s network connection. If your computer or network is protected by a firewall or proxy, make sure that Firefox is permitted to access the web. You can continue with your default DNS
-
for industry. To this end, the latest findings from the fields of artificial intelligence, machine learning and cloud-based methods are combined with proven expert knowledge to answer current questions in robot
-
Curriculum and other under and post graduate degree programmes that involve the Faculty of Medicine. Further, they will be expected to teach in areas outside their specialisation. The successful candidate will
-
Max Planck Institute of Molecular Cell Biology and Genetics | Dresden, Sachsen | Germany | about 13 hours ago
: Dresden, Saxony 01307, Germany [map ] Subject Areas: Artificial Intelligence, Machine Learning, Applied Mathematics, Biomathematics, Mathematical Biology, Computational Science, Statistics, Numerical
-
novel machine learning-guided approaches. The position is located at TUM Campus Heilbronn. Your qualifications Strong background in computer science, AI, or related areas or similar fields. Solid
-
contribution of genetic and non-genetic driving forces for the cells’ evolution and glioma development. Using multi-omics data integration and machine learning, we will investigate cellular behaviors and gene
-
electrochemical impedance spectroscopy (EIS) directly during the disassembly process to classify the cells for their reusability. A pre-trained machine learning model for assessing cell condition based on EIS data