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Field
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-based modeling and probabilistic machine learning, tackling problems that arise in molecular systems and heterogeneous materials. Eine Doktorandenstelle im Bereich physik-informiertes generatives
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learning-based surrogates for physical systems LLMs and scientific agents – large language models that autonomously reason, plan and execute scientific workflows AI for engineering design – LLM-driven agents
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exciting opportunity for you to join our team working on Scientific Machine Learning (SciML). The project focuses on developing scalable HPC algorithms and mixed-precision solvers to train neural models
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” at the Faculty of Humanities. The position is located at an office at the management of the DLA and develops the field of exploring and analyzing analogue archive collections with the help of machine learning
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Brandenburgische Technische Universität Cottbus | Cottbus, Brandenburg | Germany | about 2 months ago
to the development and the analysis of modern machine learning methods, with a focus on probabilistic modelling that enables, for example, to account for uncertainties when training neural networks, to capture complex
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Karlsruhe Institute of Technology - Institute of Applied Geosciences - Division of Geothermal Research | Karlsruhe, Baden W rttemberg | Germany | 3 months ago
constitutes most of the recorded signal. Additionally, identification of quite periods is of interest for applying ambient seismic noise interferometry. Machine learning (ML) offers a promising solution to
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and experience with modern deep learning frameworks (e.g. PyTorch) Solid background in machine learning, ideally with experience in NLP, large language models, or sequence modeling Interest in clinical
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Personalization and adaptive reasoning systems AI for Education Doctoral students will also have access to specialized courses in: Artificial Intelligence, Machine Learning, and Edge Computing Advanced
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archive collections with the help of machine learning methods and AI. The conceptualization and acquisition of third-party funding are explicitly desired. Required qualifications include a PhD or equivalent
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or complementing traditional physics-based approaches by data-driven ones, using Machine-Learning (ML). Such approaches allow enormous gains of time, in a way that can be related to the astonishing efficiency