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at least one mainstream deep learning framework (e.g., PyTorch, JAX) • Expertise in (atomistic) thermodynamic, kinetic simulations or computational chemistry • Ability to independently design and
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Intelligence, Machine Learning, or relevant fields. Strong theoretical research capability, particularly in the theoretical analysis of optimization, convergence, stability, and/or generalization of deep
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; expertise in several of the following: life cycle assessment, life-cycle cost analysis, pavement simulation, machine learning, deep reinforcement learning, and/or physics-informed modeling frameworks; and
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used by a PhD student, likely starting October 2027, to initiate development of a deep-learning architecture. The refined batch of synthetic data will be used by the PhD student to finalise the deep
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learning and physics, addressing key challenges in modern quantitative biology. The successful candidate will be responsible for: • Develop and train deep learning models (CNNs, ...) data to predict IPLSs
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Engineering, or related field. At least 3 years of relevant experience in computer vision, artificial intelligence, etc. Proficiency in programming languages such as C and Python Proficiency in deep learning
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as a member of the GHER contributing to the EU research project COMEDI in a consortium of 11 leading partners in the field of data assimilation and deep learning. A successful applicant will develop
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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Computer Science, Artificial Intelligence, Mathematics, Engineering, or a related field. Entry level candidates with demonstrated expertise in artificial intelligence (AI), machine learning, deep learning
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record of publications in reputable peer-reviewed journals in maritime transport, logistics management, machine learning, deep learning, or optimization Proficient in both written and spoken