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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
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– 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
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Master’s degree (or equivalent) in mathematics, computer science, physics, or related field. Sound knowledge in (scientific) machine learning, and knowledge in numerical analysis and numerical linear algebra
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Mathematics/ Approximation Theory to be filled by the earliest possible starting date. The Chair of Applied Mathematics, headed by Prof. Marcel Oliver, is part of the Mathematical Institute for Machine Learning
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-scale modelling, machine learning) High resolution analysis, monitoring of chemistry, structure and transformations at the atomic scale of buried interfaces and defects by correlated experimental
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protein structure analysis. Required skills: High motivation, curiosity, self-driven, critical thinking, strong team-player, good English, high interest in protein structure and machine learning. This PhD
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Neurophysiology) at Justus Liebig University Giessen. We investigate how olfactory and taste signals are processed in the insect brain, particularly in Drosophila melanogaster , and how these signals are translated
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performing data analysis of B cell receptor repertoire data using programming languages (mostly R) Successful candidates can think independently, are eager to acquire necessary new skills, and are excellent
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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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computational scheme which allows efficient and accurate computer simulations of biofabrication. Using this novel tool in collaboration with our experimental colleagues we will thrive to understand the physical