Sort by
Refine Your Search
-
Listed
-
Employer
- Fraunhofer-Gesellschaft
- Nature Careers
- Technical University of Munich
- Leibniz
- Forschungszentrum Jülich
- Free University of Berlin
- 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
-
with other research groups for chip applications, e.g. in physics, life sciences, materials sciences, medicine, and machine learning. Chip Design is also a focus of the Master′s program in Computer
-
of study. You have knowledge of artificial intelligence and its application in the analysis of company data. You have experience with Generative AI technologies (e. g. GPT models, machine learning). You are
-
plus Proficiency in Angular, TypeScript, Node.js, HTML and CSS is advantageous You have initial experience in .NET Core Framework and database, such as MySQL You have basic knowledge of machine learning
-
regulations. Furthermore, the gathered data serves as a valuable resource for machine learning applications, enabling predictive analytics and facilitating continuous improvement in coating processes through
-
a collaboration between five Helmholtz Centers (MDC, GFZ, AWI, DESY, HZB), the Berlin Institute for the Foundations of Learning and Data (BIFOLD), and three Berlin universities. To strengthen our team
-
for many applications and processes, like in self-driving cars or to prevent factory workers from being injured by heavy machines. While AI algorithms may achieve great accuracy in the detection of persons
-
), and computational modeling (deep neural networks). We apply multivariate analysis methods (machine learning, representational similarity analysis) and encoding models. Job description: This is an open
-
advanced statistical/chemometrics and machine learning tools, iv) to couple metabolome data with other omics datasets (e.g., genomics, lipidomics, metallomics, and others). Main target areas are drug
-
and machine learning / artificial intelligence methods in combination with complex network analysis tools to predict and model interactions between food and biological systems further scientific
-
, innovative research program particularly in the field of systems immunology applying novel high-resolution technologies, and/or computational analysis methods and artificial intelligence/machine learning is