Sort by
Refine Your Search
-
. Qualifications: • Completed academic university degree (Master level) in mathematics, computer sciences, physics or a related discipline • Knowledge of programming, machine learning methods, mechanistic modelling
-
modeling and computational workflows Knowledge about machine learning: statistics and deep learning Experience in data analysis, visualization and presentation Good programming skills in languages such as
-
tolerance of these novel materials and to enable a knowledge-based assessment of their suitability for future nuclear systems. At the same time, the successful candidate will contribute to maintaining
-
Description The International Max Planck Research School “Knowledge and Its Resources: Historical Reciprocities” (IMPRS-KIR) invites applications for 3 doctoral positions, to begin on September 1
-
for a motivated PhD student with a good knowledge of algorithms and theoretical computer science. The successful candidate will take part in the research and teaching activities at the Chair of Algorithms
-
, dissertation) Your profile University degree (Master’s or Diploma) in Materials Science, Physics, Materials Engineering, Nuclear Engineering, or a related field Solid knowledge of materials characterization
-
, biosciences or a related subject area sound knowledge in the fields of plant stress signaling experience in cloning and plant biology knowledge in the role of temperature signaling in biology experience in
-
comprehensive knowledge of optical properties, properties of the electronic structure, and electro-optical properties of the innovative novel d-electron element containing group-three nitride compounds, e.g. [(Sc
-
spoken English skills Following competences are desirable: Solid knowledge of solid-state (semiconductor) physics Good knowledge in surface science Experience in epitaxy and surface science methods Basic
-
(Wissenschaftszeitvertragsgesetz - WissZeitVG). The position aims at obtaining further academic qualification (usually PhD). Professional assignment: Chair of Knowledge-Aware Artificial Intelligence (Prof. Dr. Simon Razniewski