Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Heidelberg University
- <span lang="en">TUD Dresden University of Technology</span>
- Helmholtz Association of German Research Centres
- Helmholtz-Zentrum Hereon
- Max Planck Institute of Biochemistry
- Max Planck Institute of Biochemistry, Martinsried
- Technische Universität Dresden (TU Dresden)
-
Field
-
. Institute of Material Systems Modeling The Institute of Material Systems Modeling works on methods for virtual material and process development. Computational methods and artificial intelligence are
-
and Cell Biology Job Offer from September 04, 2026 25 PhD Positions (m/f/d) - Max Planck School of Biomedical AI. The Max Planck School of Biomedical Artificial Intelligence has opened the call for
-
Intelligence, Computer Science, Data Science, Computational Cognitive Science, Medical Informatics, or a comparable quantitative discipline Proven hands-on experience working with LLMs, foundation
-
Helmholtz Association of German Research Centres | Geesthacht, Schleswig-Holstein | Germany | about 2 months ago
computational models and running computer simulations in science and engineering still requires substantial expert knowledge. Agentic artificial intelligence (Al) offers a great opportunity to
-
>biochemistry, structural biology, biophysics, cell biology, systems biology, and computational biology. We are looking for outstanding graduates from all over the world to join our
-
agent) consortium, an EIC Pathfinder Challenge project on generative AI agents for cancer diagnosis and treatment. We are now expanding our activities in translational artificial intelligence and are
-
<span lang="en">TUD Dresden University of Technology</span> | Dresden, Sachsen | Germany | 12 days ago
SystemC and field-programmable gate arrays (FPGAs)
- developing machine learning methods to identify suitable computational kernels and optimize their placement within the processor memory
-
architectures for RISC-V processors using SystemC and field-programmable gate arrays (FPGAs) developing machine learning methods to identify suitable computational kernels and optimize their placement within the