31 data-"https:" "https:" "https:" "https:" "https:" "https:" "UNIV" "UNIV" "UNIV" PhD positions at Nature Careers in Germany
Sort by
Refine Your Search
-
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
-
(https://www.cliccs.uni-hamburg.de/about-cliccs/cliccs-ll.html ). In CLICCS-M4, we are further developing the unique ICON-Coast model within the ICON Earth System Modelling Framework. The objective
-
for which your data will be processed, as well as further information about data protection is available to you on the website: https://tu-dresden.de/karriere/datenschutzhinweis .
-
regeneration. We combine these approaches to bridge the gap between fundamental research and clinical therapies. More information about our research group can be found here: https://tud.link/vprn . TUD strives
-
allowance. The employment will initially be limited to three years. Important: Applicants must not have resided in Germany for more than 12 months in the past three years. Group website: https://biophys.uni
-
collect and analyse health data, identify risks, advise government and experts, and develop new scientific methods. We are based in Berlin, Wildau and Wernigerode. Get started now Apply directly through the
-
at the Institute of Medical Informatics within the research group “Medical Data Integration Center (MeDIC)” led by Dr. Michael Storck and is embedded in the DFG-funded Collaborative Research Centre 1748, Principles
-
Münster, the CoBIC Frankfurt am Main, the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig and the Johannes Gutenberg University Mainz. RESPONSIBILITIES: Data collection using
-
, CRISPR-Cas systems, microRNAs, non-coding RNA, RNA biology of infections, and RNA chemistry. Applicants can choose a mentor who best matches their interests and background (more information under “Panel
-
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