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, with machine-learned interatomic potentials fine-tuned on in-house DFT calculations. The aim is to identify the elementary steps that control catalytic activity, and to predict how it responds to changes
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Postdoc: Machine learning for wind flow prediction in coastal dunes Faculty: Faculty of Geosciences Department: Department of Physical Geography Hours per week: 36 to 40 Application deadline: 6
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learning – and we are growing and seeking: Postdoc / PhD Student / Entrepreneurial Postdocs to join the Institute and its wider research community. We welcome applications across all areas of machine
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the division of Data Science and AI , we develop data-driven methods and AI solutions that support intelligent decisions across society, advancing machine learning techniques, from foundations to industrial and
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the division of Data Science and AI , we develop data-driven methods and AI solutions that support intelligent decisions across society, advancing machine learning techniques, from foundations to industrial and
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Inria, the French national research institute for the digital sciences | Grenoble, Rhone Alpes | France | 10 days ago
communication, sociable with an appetite for working in a group. Additional skills appreciated: rigorous, organized, curious, autonomous, proactive and dynamic. A specialization in optimization, machine learning
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project focused on developing an advanced machine learning framework for spatio-temporal datasets. The position is for 2.5 years and is partially funded by the Dutch Research Council (NWO) through
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established lasting ties with industrial partners and created several spin-off companies. CONTEXT AND MISSION We are seeking a postdoc to join the Quantum Machine Learning team (QML-CVC) in beautiful Barcelona
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of Prof. Georg Madsen, with regular shorter research stays at Aarhus University. The project combines density functional theory (DFT), machine-learned force fields and atomistic simulations to uncover how
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Do you want to contribute to the future of AI-driven electric transport systems? Join our research group to develop advanced machine learning methods for electromobility, focusing on energy-aware