77 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" "Univ" "Univ" "Univ" PhD scholarships at Nature Careers
Sort by
Refine Your Search
-
Listed
-
Country
-
Field
-
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
-
learning Distributed and federated training The candidate is expected to hold a relevant MSc degree in Computer Science, Data Science, Physics, (Applied) Mathematics, Computational Statistics or another
-
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
-
Learning with Graphs led by Prof. Nils M. Kriege. Our research focuses on the development of new methods and learning algorithms for structured data. Graphs and networks are ubiquitous in various domains
-
(SNSPD). Additional responsibilities include developing efficient coupling of free-space optics to optical fibers, conducting extended data-taking runs with TES and SNSPD systems, and performing data
-
applications of macrosopic quantum coherence in levitated solidstate platforms. Our Team is part of the Quantum Optics, Quantum Nanophysics and Quantum Information group of the Faculty of Physics. We are member
-
, 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
-
analysis to extract information on atomic dynamics from image series. Investigate molecular adsorption, surface reconstruction and site-dependent reactivity at the single-atom level Understanding of atomic
-
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
-
materials synthesis, advanced operando characterization, and lab scale testing. We use robotic, high-throughput methods, and data science to accelerate novel sustainable materials discovery. PhD Position