Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Forschungszentrum Jülich
- Technical University of Munich
- DAAD
- Hannover Medical School •
- Leibniz
- University of Stuttgart
- Academic Europe
- Bielefeld University
- Deutsches Elektronen-Synchrotron DESY •
- Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt
- Max Planck Institute for Human Cognitive and Brain Sciences •
- Max Planck Institute for Sustainable Materials •
- Nature Careers
- Saarland University
- University of Stuttgart •
- University of Tübingen
- 6 more »
- « less
-
Field
-
local activation), multi-timescale adaptation (local memory), and stimulus-specific adaptation (multi-task processing). While the co-optimization of dendrite-inspired functional circuits with emerging
-
Applicants must be eligible to enroll on a PhD program at US (https://www.uni-stuttgart.de/en/research/early-career-researchers/doctoral-degree-studies/ and https://www.f06.uni-stuttgart.de/en/research/phd
-
candidate will be embedded in the DFG Research Program RTG3120 on Biomolecular Condensates ( https://dresdencondensates.org ). Each PhD project is part of an interdisciplinary framework that includes shared
-
degrees at Forschungszentrum Jülich (including its various branch offices) is available at https://www.fz-juelich.de/en/careers/phd We welcome applications from people with diverse backgrounds, e.g. in
-
working in interdisciplinary and international teams and have image processing or image analysis skills. In addition, you are able to express yourself confidently both orally and in writing in English. What
-
physics, biomedical/material engineering or a related discipline. You have a strong background in data analysis and image processing. You enjoy working in interdisciplinary and international teams and have
-
tools for distributed models, and iii) robustness to data and model poisoning attacks. In this context, we are looking for a PhD Candidate who has a strong background in machine/deep learning to push our
-
tools for distributed models, and iii) robustness to data and model poisoning attacks. In this context, we are looking for a PhD Candidate who has a strong background in machine/deep learning to push our