Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Technical University of Munich
- Forschungszentrum Jülich
- DAAD
- Leibniz
- Nature Careers
- University of Tübingen
- Universität Tübingen
- Academic Europe
- Brandenburg University of Technology Cottbus-Senftenberg •
- Deutsches Elektronen-Synchrotron DESY
- Deutsches Elektronen-Synchrotron DESY •
- GFZ Helmholtz-Zentrum für Geoforschung
- Hannover Medical School •
- Helmholtz-Zentrum Geesthacht
- Helmholtz-Zentrum Hereon
- Leibniz-Institut für Präventionsforschung und Epidemiologie – BIPS GmbH
- Max Planck Institute for Sustainable Materials •
- RPTU University of Kaiserslautern-Landau •
- Technische Universität Berlin •
- University Hospital of Schleswig Holstein
- 10 more »
- « less
-
Field
-
/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages
-
science/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning
-
for the analysis of large-scale omics datasets Multi-modal machine learning model development and data integration Participating in the Integrated Research Training Group ‘Reproduction.MS PhD-Training Centre in
-
field * Strong background in mathematical and computational sciences * Experience with large-scale machine learning, foundation models, or data-centric AI is a plus * Driven, with a strong work ethic and
-
Exciting and high-profile interdisciplinary research on visualisation, machine learning, and human-computer interaction Comprehensive computer infrastructure for AI and the analysis of large data volumes A
-
acceleration of large-scale machine learning workloads Perform characterization and modeling of electronic and optical devices Develop hardware-aware machine learning models incorporating electronic and optical
-
: Investigate and design optimal computing and communication architectures for hardware acceleration of large-scale machine learning workloads Perform characterization and modeling of electronic and optical
-
research group “Machine Learning for Biomedical Data” led by Prof. Dominik Heider and is embedded in the DFG-funded Collaborative Research Centre 1748, Principles of Reproduction. The CRC 1748 involves
-
the reference number 27697, via our online portal: Apply now via https://jobs.uksh.de/job/Kiel-PhD-%28mfd%29-Statistical-Genetics-Machine-Learning-Schl-24105/1279933701/ For more information visit: www.uksh.de
-
-phd-positions/ . Requirements Top-ranked Master's degree in robotics, computer vision, system control, machine learning, mathematics, or a related field (background in any of the following); Being