Sort by
Refine Your Search
-
Listed
-
Employer
- Fraunhofer-Gesellschaft
- Technical University of Munich
- Nature Careers
- Forschungszentrum Jülich
- Free University of Berlin
- Leibniz
- DAAD
- Heidelberg University
- Helmholtz-Zentrum Berlin für Materialien und Energie
- Katholische Universität Eichstätt-Ingolstadt
- Max Planck Institute for Brain Research, Frankfurt am Main
- Menlo Systems GmbH
- 2 more »
- « less
-
Field
-
their reliability and resource efficiency during production and operation. The »KI-unterstütze Simulation« team combines physically based simulation approaches with efficient and advanced mathematical algorithms and
-
be achieved, for example, by developing learning algorithms and bringing together different sensor systems in the vehicle and on the road. Where you put the focus - that is up to you. The concepts
-
, machine learning algorithms, and prototypical energy management systems (EMS) controlling complex energy systems like buildings, electricity distribution grids and thermal energy systems for a sustainable
-
properties, 2D-materials like graphene offer the ideal material basis for new, innovative applications for electronic and sensor components, such as IR-emitter, modulators or integrated heaters. Still, their
-
of skills, covering the entire chain of optronics and sensor data evaluation and ranging to the engineering of secure and user-adequate systems. The employment is on a fixed-term 2 year contract with
-
) Mathematical Modeling, Optimization, and Simulation Classical Image Processing and Machine/Deep Learning Probalistic Sensor Data Processing ( Kalman Filter, etc.) What you can expect A dynamic work environment
-
thawing, sea level budget, climate and earth system modeling, soil parameter mapping, and multi-sensor segmentation, together with our partners at renowned international and national institutes such as Bonn
-
combine electronic components, micro and nano sensors and actuators with interfaces for communication. Fraunhofer ENAS develops individual components, the technologies for their production as well as system
-
processing workflows including QC and reproducibility metrics * APIs and packages supporting the development of new algorithms spanning large * language modeling of DNA and RNA sequences, and algorithms
-
storage Innovation in the Machine Learning algorithms for EDA in terms of Computational Complexity, Performance Scores, etc. To learn more about our previous work, please check out our website