-
of neural hydrology, where hydrological models are directly learned from data via machine learning (e.g., LSTM neural networks, [1]). Initially, these models ignored all physical background knowledge and did
-
/NL. The successful candidates commit to actively participate in networking including regular research visits to the partner laboratories. Requirements: university degree in chemistry or physics and
-
represent stakeholder preferences. The integrated Research Training Group (RTG) will provide doctoral researchers with an attractive qualification program, foster networking, enable internationalization and
-
– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1
-
between quantum computers (via Qiskit) and classical HPC resources Validate the QCS-MiMiC implementation on IBM’s ibm_cleveland quantum computer by reproducing recently published benchmark QM / MM
-
represent stakeholder preferences. The integrated Research Training Group (RTG) will provide doctoral researchers with an attractive qualification program, foster networking, enable internationalization and
-
parametrization, estimate N-mineralization rates from soil organic matter content and other N-balance components support monitoring, reporting and verification of greenhouse gas emissions and mitigation efforts
-
represent stakeholder preferences. The integrated Research Training Group (RTG) will provide doctoral researchers with an attractive qualification program, foster networking, enable internationalization and
-
breakage models, e.g. with stochastic tessellations Development and implementation of estimation methods for the model parameters, e.g. with machine learning or statistical methods Lab work and collection
-
working, research, and networking possibilities. The position will be in the group of Prof. Thomas Heine at the Chair of Theoretical Chemistry where 40 researchers from 9 nations works interdisciplinary in