Sort by
Refine Your Search
-
Category
-
Employer
- Technical University of Munich
- Leibniz
- Forschungszentrum Jülich
- Helmholtz
- ; Berlin Institute of Health
- FIZ Karlsruhe – Leibniz-Institut für Informationsinfrastruktur
- GESIS - Leibniz Institut für Sozialwissenschaften
- Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt
- Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association
- Helmholtz-Zentrum Potsdam - Deutsches GeoForschungsZentrum GFZ
- Max Planck Institute for Biogeochemistry, Jena
- University of Bremen
- University of Duisburg-Essen
- 3 more »
- « less
-
Field
-
Postdoc candidate. The selected candidate will have the opportunity to work closely with PhD students. Your profile: The successful candidate must hold a PhD degree in computational science including
-
coordination of multiple visual areas. Requirements: Ideal candidates will have a PhD in computational neuroscience, physics, computer science or related fields. They must have a strong background in neural
-
the project. An exciting and varied job where you will gain deep insights into a research institute as well as national and international science systems. At DIW Berlin, you will have the perfect opportunity
-
Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | about 1 month ago
conferences) Your profile # Completed university studies (PhD) in the field of Medical Physics or a related subject and having a strong data science background # High motivation to work in a collaborative
-
towards a better understanding of Earth system dynamics. In particular, we are actively involved in researching the use and development of machine and deep learning (ML/DL) approaches to model, parameterize
-
publications Training and co‑supervision of Master or PhD students within the group in molecular epidemiology/deep learning techniques What you bring along: Completed Ph.D. in Data Science (deep learning
-
, depending on the geographical and economic context. It will include a deep dive on the potential of Ukraine to become a green hydrogen hub, leveraging geo-spatial energy models run by project partners. As
-
respect to the identification and functional annotation of genes involved in, for example, cell wall degradation, carbon flux etc. Screening metagenomic and genomic data using deep learning methods in order
-
genomic data using deep learning methods in order to identify degrading enzymes from different data resources Using Hidden Markov Models and similar tools as well as machine learning for the identification
-
supported by more junior colleagues (PhD candidates, Bachelor’s and Master’s students). Requirements PhD degree in Computational biology or related (Computer Science, Physics) Experience with NGS data