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Field
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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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of Immunology) of the University Medicine Oldenburg to perform analyses using machine learning methods and to establish multimodal prediction models in research, teaching, and patient care in the long term
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FAU DCN-AvH. Chair for Dynamics, Control, Machine Learning and Numerics | Germany, | Germany | about 2 months ago
5 Mar 2025 Job Information Organisation/Company FAU DCN-AvH. Chair for Dynamics, Control, Machine Learning and Numerics Department Department Mathematik Research Field Mathematics Researcher Profile
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Description The Soft Matter Physics Group at the University of Tübingen is searching for a PhD student / doctoral candidate in Physics, Machine Learning and advanced X-ray and neutron scattering (m
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artificial intelligence methods. PhD position in atmospheric corrosion studies via novel experiments and machine learning Reference code: 50134137_2 – 2025/MO 1 Commencement date: as soon as possible Work
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employment. Starting date: 10.02.2025 Job description:PhD position in atmospheric corrosion studies via novel experiments and machine learning Reference code: 50134137_2 ? 2025/MO 1 Commencement date: as soon
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13.03.2025 Application deadline: 30.06.2025 The Soft Matter Physics Group at the University of Tübingen is searching for a PhD student / doctoral candidate in Physics, Machine Learning and advanced
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looking for a PhD Student (f/m/d) in Machine Learning for Quantum Computing and Simulation of Quantum Matter. The Scope of Your Job The successful candidate (f/m/d) will be part of the interdisciplinary
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and data analytics (including machine learning and deep learning); from high-performance computing to high-performance analytics; from data integration to data-related topics such as uncertainty
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PhD position in interpretable machine learning for dementia prediction. The project focuses on developing interpretable deep learning models for dementia prediction using multi-modal data, including MRI