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Field
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-based modeling/data analytics/deep learning are preferred. Key Responsibilities: Strong candidates should take the lead of at least one of the following responsibilities: Soil organic carbon model
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-of-the-art machine learning/deep learning models for RNA sequence design, structure prediction, and property optimization. Process, curate, and model high-throughput screening and genomics datasets
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, multimodal foundation models, computer vision, or related deep learning methods. A good understanding of efficient model design, model compression, and optimisation for edge or embedded deployment. Proficiency
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publications. Requirements: PhD in Computer Science, AI, Data Science, Materials Science/Engineering, Chemical/Mechanical Engineering or a related field. Strong machine-learning/data-science or computational
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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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Supervise and mentor research associates, engineers, and PhD students working on the project Collaborate with perception, controls, systems, and hardware teams to guide integration of learned policies
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quantitative discipline, completed before the appointment begins. Strong Python programming skills and hands-on experience in machine learning or deep learning. Demonstrated ability to conduct original research
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other duties related to the research program. Job Requirements: A PhD in Computer Science or a relevant field. Strong background in deep learning, Generative AI, and multimodal learning. Strong
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Fellowship Description The Basque Center for Applied Mathematics (BCAM) is launching two postdoc positions of 2 years each within BCAM–Helsinki Lab on Machine Learning and Inverse Problems, a
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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