Sort by
Refine Your Search
-
Listed
-
Employer
- DAAD
- Helmholtz-Zentrum für Umweltforschung - UFZ
- Technical University of Munich
- Leibniz
- Nature Careers
- Forschungszentrum Jülich
- Fraunhofer-Gesellschaft
- Heidelberg University
- Technische Universitaet Dresden
- Universität Siegen
- ;
- Academic Europe
- Carl von Ossietzky Universität Oldenburg
- Friedrich-Alexander-University Erlangen-Nürnberg
- GESIS - Leibniz Institut für Sozialwissenschaften
- Goethe-Universityrankfurt
- Hertie AI institute for brain health / University of Tübingen
- Kiel University;
- Lehrstuhl für Nachhaltige Thermoprozesstechnik und Institut für Industrieofenbau und Wärmetechnik
- University of Hamburg
- University of Mannheim
- University of Stuttgart
- University of Tübingen
- Universität Bielefeld
- 14 more »
- « less
-
Field
-
++, Python, and JavaScript languages, multi- and many-core SoC, RISC-V, hardware synthesis, hardware-software co-design, (meta-heuristic) optimization algorithms, machine learning frameworks, (bonus topics
-
improvement of digital learning materials (e.g., video tutorials, exercises, guides, and documentation) in collaboration with content experts Monitoring and evaluation of the online training offer to enhance
-
the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission analysis, and infrared thermography
-
models. Your tasks: Research, development, and evaluation of Machine Learning and Deep Learning methods Prototype development Literature review Publication and presentation of scientific results in
-
Lehrstuhl für Nachhaltige Thermoprozesstechnik und Institut für Industrieofenbau und Wärmetechnik | Aachen, Nordrhein Westfalen | Germany | 18 days ago
. Methodological knowledge in the field of machine learning is an advantage. You have a high level of independence and commitment. You would like to develop and realise your own ideas. You enjoy working in a team
-
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
-
These are positions for Doctoral Students, based in Tübingen in an interdisciplinary research group working at the interface of Machine Learning, Medicine, and Biology. Doctoral Students will engage
-
therapeutics by protein design. This project will apply cutting-edge generative AI methods—including protein design, structure–function prediction, and multimodal learning—to develop and optimize a new
-
and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with a strong team
-
industry partners in the department of the same name to further develop LPBF for a wide range of industrial applications – from new machine concepts and innovative process strategies to the processing