549 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" PhD positions
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Field
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or Phonetics Basic knowledge of machine learning tools; familiarity with a scripting language Ability to communicate and coordinate with different partners: field linguists, computer scientists, engineers
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acceleration of large-scale machine learning workloads Perform characterization and modeling of electronic and optical devices Develop hardware-aware machine learning models incorporating electronic and optical
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systems, and space applications. We combine theory, physics-based simulations, machine learning, and autonomous workflows to understand and design materials that can perform under conditions where
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combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real-world energy applications, the project aims to better capture the dynamics of urban infrastructures
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) extremes, preventive methods for mitigation of marine-induced hazards, application of machine learning techniques and opportunities provided by AI. Even though the listed advanced topics are mostly at the
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is made for you! Information We invite highly motivated students with a strong background in mathematical control theory, and a keen interest in machine learning to apply for the PhD position within
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Job related to staff position within a Research Infrastructure? No Offer Description PhD position on physics-based machine learning modeling for materials and process design Reference code: 2026/WD 1
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 2 days ago
infrastructure you will have the opportunity to make an important contribution for a healthier society. The Theis Lab has a long-standing reputation for pioneering machine learning and AI methods in molecular
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the project, supported by Dr. Adamo and close collaboration partners, within an environment that encourages academic freedom and scientific independence. In line with our and Uni Basel values (https
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performance data using recommended guidelines and machine learning tools Defining the uncertainty sources Enhancing existing guidelines for full-scale power-speed assessment practice Disseminating research