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mathematics, or a closely related field. Relevant experience may include one or more of the following: machine learning, deep learning, or generative modelling computer vision or biological image analysis
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PhD in machine learning, artificial intelligence, computer vision, robotics, biomedical engineering, computer science, biomechanics, neuroscience, signal processing, or a closely related discipline
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experimentation and training. Science of Deep Learning: Exploring mechanistic interpretability and understanding the fundamental drivers of model performance at scale. As an early member of this fast
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theoretical and practical knowledge of methodologies such as deep and statistical learning. Experience of executing research projects at the level of senior PhD student, including creating and
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(Computer Science, theory, empirical, or Science). Excellent academic record required Solid programming foundation, familiar with mainstream deep learning frameworks such as PyTorch. ACM/ICPC or NOI/IOI
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learning, particularly with deep learning, perception models, and generative AI. • High proficiency in Python alongside experience using a deep learning framework, with a strong preference for PyTorch
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combining state-of-the-art machine learning with physicochemical knowledge and molecular modeling. Representative publications from our group include: https://doi.org/10.1038/s41467-025-63947-5 https
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combining state-of-the-art machine learning with physicochemical knowledge and molecular modeling. Representative publications from our group include: https://doi.org/10.1038/s41467-025-63947-5 https
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areas: spin physics of point defects, machine <b>learning</b> for materials discovery, multi-scale simulation methods, and excited-state quantum chemistry. For more details, please visit <b>https</b>://pjadaun.com/open-positions/ . Please send applications to [email protected] ,...
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Biotech Research and Innovation Centre, The Lund Group, University of Copenhagen | Denmark | about 1 month ago
project is conducted in collaboration with the deep probabilistic programming group of Thomas Hamelryck: https://di.ku.dk/english/research/groups/machine-learning-in-biology/?pure=en/persons/271052