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combine robot–plant interaction with multi-modal perception and machine learning. Current noninvasive agricultural monitoring systems rely primarily on passive sensing, which limits sensitivity to early
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from Okinawa, Japan, to investigate how species interactions and network structure shape response diversity under environmental change. The project will develop and apply AI and machine-learning
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. Doctoral studies end with a thesis and a doctoral degree. More about being a doctoral student at LTH on lth.se. https://www.lth.se/english/study-at-lth/phd-studies/ AEGIS — Adaptive Grid-Interactive
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. In connection to this, the position includes the opportunity for three weeks of training in higher education teaching and learning. The postdoctoral fellow will: Develop and maintain harmonized
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across the university. The successful candidate will also contribute to the development of teaching and learning within the subject area. The research can be based on controlled experiments as well as
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Computational Fluid Dynamics (CFD), fluid mechanics, and Artificial Intelligence (AI), with a particular focus on developing deep reinforcement learning methods for active flow control of hydraulic
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simulations (QM/MM and atomistic molecular dynamics simulations, free energy methods, and machine learning). Candidates with either computational and/or experimental expertise are encouraged to apply
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assignments in engineering programs and master's programs. More information is available on our website. (https://kemi.uu.se/angstrom/?languageId=1).About the positionApplications are invited from
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and Engineering, we are seeking a researcher with a strong interest in developing and applying machine‑learning methods for materials design, in particular steel design. The position is part of our
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experimentally. The research will be broadly situated in machine learning, including (but not limited to) algorithmic knowledge discovery, graph mining and social network analysis, optimization for machine