Sort by
Refine Your Search
-
Listed
-
Employer
- Technical University of Munich
- Nature Careers
- Leibniz
- Deutsches Zentrum für Neurodegenerative Erkrankungen
- Free University of Berlin
- Heidelberg University
- Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association
- Leibniz Institute for Neurobiology
- Max Planck Institute for Dynamics and Self-Organization, Göttingen
- University of Cambridge
-
Field
-
Postdoctoral Research Associate in Sum-Frequency Generation Microscopy of Biomolecular Self-Assembly
the institute focusses on the structure and dynamics of elementary processes in solids and at surfaces. Within this broader research theme, the Nonlinear Chemical Imaging (NCI) group of Dr. Alexander Fellows
-
Max Planck Institute for Dynamics and Self-Organization, Göttingen | Gottingen, Niedersachsen | Germany | 3 months ago
develop our state-of-the-art imaging systems by optimizing the hardware and integrating real-time data processing and analysis through machine learning techniques to achieve precise characterization
-
physics, engineering, medicine, biology or a related field Practical experience in optics, signal processing and working with experimental setups (e.g. MATLAB) Interest in medical diagnostics and innovation
-
efficiency while keeping the grid reliable and secure. Our research method is engineering-oriented, prototype-driven, and highly interdisciplinary. Our typical research process includes the evaluation
-
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
-
collaborate closely with a dedicated team of soil fauna experts, ecological data modelers, computer-vision system engineers. Your Tasks Establish data science pipelines, data-modelling strategies, model
-
of Schleswig-Holstein (10 %) and is one of the internationally leading institutions in the field of marine sciences. Through our research and our commitment to the transfer of knowledge and technology, we
-
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
-
energy efficiency while keeping the grid reliable and secure. Our research method is engineering-oriented, prototype-driven, and highly interdisciplinary. Our typical research process includes
-
-)Statistics, (Bio-)Informatics, Computer Science or related disciplines Strong background in modeling multi-modal data (images, tables, text, etc) Understanding of biases and causal inference Experience with