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. This is a fixed-term position to end 24 months after the start date. Job description The postdoc position focuses on physics-driven machine learning methods for ground-based, airborne and drone-based
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materials science, and machine-learning-driven materials discovery. The successful candidate is expected to have experience in areas such as high-throughput DFT calculations, thermodynamic and phase
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data. Manage preferences for further information and to change your choices. Accept all cookies Reject optional cookies Skip to main content Postdoc Position in Neuro-morphic Reinforcement Learning
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which you will be automatically redirected to an external site after 5 seconds) Save job Click to add the job to your shortlist Send job Job Details Are you interested in using machine learning to
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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
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reactor. The work combines physics-based thermal design and process-level system simulation with high-fidelity computational fluid dynamics and fast reduced-order and machine-learning models, so that the
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Postdoc in first-principles modelling and machine-learned force fields for correlated disorder in...
data. Manage preferences for further information and to change your choices. Accept all cookies Reject optional cookies Skip to main content Postdoc in first-principles modelling and machine-learned
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, machine-learning methods and predictive calculations of defects and transport properties. The position will also be integrated into the wider CENSEMAT research environment at Aarhus University, allowing
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Two postdoc positions in Thermofluid Design and Digital Modeling of Cooling Systems for Electroch...
process-level system simulation with high-fidelity computational fluid dynamics and fast reduced-order and machine-learning models, so that the final design is both experimentally realizable and
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structured, well-documented way that supports benchmarking, reproducibility, and reuse by others. Essential: Strong data analysis and machine learning skills and experience with PyTorch (or equivalent