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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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Description Are you interested in developing novel scientific machine learning models for a special class of ordinary and differential algebraic equations? We are currently looking for a PhD
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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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: Develop innovative machine learning architectures for the mining, prediction, and design of enzymes. Combine state-of-the-art ML (e.g., deep learning, generative models) with computational biochemistry
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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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of the PhD topic (subproject A7- Reinforcement learning for mode choice decisions): This PhD project will develop and implement a Deep Reinforcement Learning (DRL) model for dynamic mode choice within
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, geometallurgy or related field Experience in either stochastics, deep learning or minerals processing is needed Structured and solution-oriented working style, analytical thinking and above-average committment
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collaboration with Q.ANT GmbH in Stuttgart, a deep-tech company that develops photonic computing and photonic sensing products. The goal of this project is the development of highly integrated vapor cells with
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research and supervision in an interactive and flexible setting? The research group of Jun.-Prof. Dr. Endo, just launched in May 2025, is looking for highly motivated candidates for PhD or postdoctoral
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disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with