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, and advanced machine learning in the engineering domain. Generative AI substantially changes the way engineers interact with and benefit from AI and access domain-specific knowledge, marking a phase
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machine learning and computer simulations. The focus of the PhD project will lie on developing machine learning models for clustering, classification, regression and reinforcement tasks to work with
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interdisciplinary research environment at the interface of computational mechanics, mechanical/civil engineering, and scientific machine learning. The PhD candidate will also have opportunities to present their work
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following criteria: PhD in Computer Engineering, Computer Science, Electrical Engineering, or a closely related field Demonstrated research excellence, evidenced by peer-reviewed publications Expertise in
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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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-resolution (SR) technologies influence human and machine-based facial identification. The PhD will combine behavioural experiments, machine learning, and explainable-AI methods to answer questions: 1. Do SR
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/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages
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a wider ERA Chair team in interdisciplinary aspects such as microfluidic design and machine learning, however it is anticipated upon completion of the PhD the candidate will be positioned as a world
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the DFG Priority Programme “Molecular Machine Learning” and embedded in the research project “Multi-fidelity, active learning strategies for exciton transfer in cryptophyte antenna complexes”. The PhD
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a PhD in machine learning, math, stats, physics, or some other technical area by the time the position starts. Additional Qualifications Candidates should have significant experience in some area of