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Field
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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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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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, 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
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UiO/Anders Lien 16th October 2026 Languages English English English PhD Research Fellow in Machine Learning and Statistics Apply for this job See advertisement About the position Integreat
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or connectivity analysis; machine-learning or deep-learning methods for geospatial analyses; ecological or remote-sensing fieldwork, particularly in alpine environments; Google Earth Engine, geodatabases or cloud
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++, Java, Julia, or other competent languages. A good record of publications in reputable peer-reviewed journals in maritime transport, logistics management, machine learning, deep learning, and optimization
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Location: The Francis Crick Institute, London Short summary We are seeking an ambitious Postdoctoral Fellow to develop the next generation of deep mechanistic models (DMMs; Fabrini & Fröhlich