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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
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these micropollutants, per- and polyfluoroalkyl substances (PFAS) are of particular concern. Like microplastics, PFAS are highly persistent, mobile, and widely distributed, even in remote areas. Both substance groups
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learning and data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms
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the environment, including traffic conditions, travel time, and cost. The project will define the DRL components (states, actions, rewards, policies), select and implement suitable DRL algorithms, and integrate
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, C/C++ and/or Java, etc.; experience with the implementation of specialized transport modelling software, optimization algorithms and procedures; strong ability and desire to learn new programming
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data Development of algorithms for infection and evaluation of infection hotspots in the plant population Coordination of the scientific interface to the project partners with regard to entomological
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inviting applications for a PhD Student (f/m/x) for the project Theory and Algorithms for Structure Determination from Single Molecule X-Ray Scattering Images Project description Single molecule X-ray