Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Technical University of Munich
- Nature Careers
- Leibniz
- Forschungszentrum Jülich
- Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung
- Max Planck Institute for Multidisciplinary Sciences, Göttingen
- University of Tübingen
- Free University of Berlin
- Friedrich Schiller University Jena
- Fritz Haber Institute of the Max Planck Society, Berlin
- Helmholtz-Zentrum Berlin für Materialien und Energie
- Helmholtz-Zentrum Geesthacht
- Leibniz Institute for Neurobiology
- Max Planck Institute for Mathematics in the Sciences
- Max Planck Institute for Plasma Physics (Greifswald), Greifswald
- Max Planck Institute for the Structure and Dynamics of Matter, Hamburg
- University of Paderborn
- WIAS Berlin
- 8 more »
- « less
-
Field
-
disciplines (typically mathematics, physics). For Postdocapplicants: Excellent track recordin computer science or engineering. Fluency in spoken and written English is required. Proficient in at least one
-
field such as computer science, bioinformatics, mathematics, computational life sciences, or related. Profound knowledge in machine learning, preferably deep learning for image data. Experience in
-
performance and degradation of electrolysis in dependence on different operating modes Your Profile: Completed Master’s degree in chemical engineering, computational engineering, computational mathematics, data
-
good publication track record Above-average master’s degree in computer science, electrical/ mechanical engineering, applied mathematics, or a similar engineering-oriented quantitative discipline
-
mathematics, computer science, information technology, electrical engineering, physics, mechanical engineering, or a comparable qualification Sound knowledge of mathematics and physics, especially in the fields
-
, computer science, mathematics, physics, or a related field with an outstanding academic record. Interest in mathematical signal processing, optimization, and/or machine learning is important. Since
-
background in a technical field such as computer science, bioinformatics, mathematics, computational life sciences or related. Profound knowledge in machine learning, preferably deep learning for image data. A
-
14.12.2022, Wissenschaftliches Personal The BMBF-funded position is part of the CoMPS project, which is a multidisciplinary project combining the fields of mathematics, computer science, geophysics
-
. Your qualifications ▪ Above-average university degree in electrical engineering, communications engineering, mathematics, physics (or similar) with thorough knowledge in quantum information and
-
. Your qualifications An excellent PhD degree either in Computer Science, Physics, Mathematics or related fields, ideally with a background in quantum theory, quantum computing or quantum machine learning