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Postdoctoral Researcher in Machine Learning of Isomerization in Porous Molecular Framework Materials
broad range of applications. Computational chemistry and Machine Learning increasingly underlies MFM research to search or screen candidate MFMs prior to synthesis. A major drawback when applying
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Location: South Kensington Campus About the role: The Robot Learning Lab in the Department of Computing at Imperial College London is seeking a talented Research Assistant (pre-doc) for an ambitious
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interdisciplinary research with leading academic groups across neuroscience, physics, and computer science, contributing directly to the future of neuromorphic systems. Key Responsibilities: Differentiate, grow and
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, transitioning it from a promising research tool to a commercially attractive platform. Prof. Knowles' research group uses experimental and computational biophysics and physical chemistry to study human health and
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topics would be advantageous: open system quantum optics, many-body physics, cold atom physics, computational physics, quantum physics, nano-photonics and metamaterials. The appointment will be for two
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Hydrogen Partnership project through the HORIZON programme investigating direct electrolysis of seawater to generate green hydrogen. You will join a consortium of 8 partners from 6 countries dedicated
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publications in journals relevant to international political economy, digital governance, or public administration Job location This position is physically based at Aalto’s Department of Computer Science, one
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to completion) in atmospheric physics or related fields. Experience in atmospheric physics and track record in atmospheric modelling and the constraint of atmospheric models with observations are essential as
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About the Role We are looking for a Postdoctoral Research Assistant to work with Dr Chema Martin on a Human Frontiers Science Program Research Grant project entitled “Evolutionary Biophysics
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About the Role We are seeking a researcher for a new programme focused on improving understanding of cancer risk and developing novel multicancer risk prediction models to support cancer prevention