Sort by
Refine Your Search
-
well as analytical methods are an advantage Willingness to handle unsealed radioactive materials within a radiation-controlled laboratory environment Knowledge of handling radioactive materials and radiochemical
-
these determinants, we will harness the diversity of aspartic proteases from the model plant Arabidopsis thaliana and deploy chemical synthesis, advanced modelling, protease biochemistry, mass spectrometry and
-
available on site for the development of suitable radiotracers. One focus of the work is on the use and evaluation of large tomographic data sets to derive parameter data for reactive transport modeling
-
systems using various tools and models, including: i) characterization of the emerging patterns in physical systems (solid state materials and active systems); ii) investigation of the mechanical properties
-
management platform that connects institutes to facilitate a rapid and efficient exchange among experimental and computational groups Devising an approach in invertible predictive modeling that links
-
play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms
-
-B4 Investigators: Prof. Dr. Meng Wang, Chair of Traffic Process Automation , and co-supervised by another expert in traffic control Requirements: excellent or very good university degree
-
scientific computing is a plus Strong interest in quantum computing and molecular simulations Willingness to work in an interdisciplinary, international team Fluent command of written and spoken English High
-
: Prof. Dr. Steven Travis Waller, Chair of Transport modeling and simulation, and co-supervised by at least one additional professor, plus an international tutor of the CRC Requirements: excellent
-
plant genetic mechanisms that coordinate mycorrhizal interactions with plant P and water status, root system development, and soil microbial communities. Using maize and rice as models, we will: 1