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Field
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following skills: Strong interest in the field of neuroimaging, psychiatry and genetics. Computer skills: Strong level in the main informatics software (FSL, Freesurfer, fMRIprep) and coding languages (R
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wireless systems using the new channel sounding. Challenge 2: Different method for spectrum sharing need to be identified for potential deployment in networks and in network planning tools and modelling
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Entitlement: 33 days annual leave, plus 9 buildings closed days for all full time staff. Use our total rewards calculator: https://www.hw.ac.uk/about/work/total-rewards-calculator.htm to see the value
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physics-informed and physics-ML hybrid approaches that integrate domain knowledge with data-driven methods to advance hydrological process understanding and prediction. Conduct multimodal, multiscale data
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, we offer a dynamic, collaborative ecosystem. The AI and Machine Learning in the Natural Sciences (AIMLeNS) lab is a tight-knit team of computer scientists, chemists, physicists, and mathematicians
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enables the application of phase contrast methods with a spatial resolution below 50 nm. Join the Cluster of Excellence "BlueMat: Water-Driven Materials" (www.tuhh.de/bluemat ) and contribute to one
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first-principles multiphase flow descriptions with data-driven components Formulate and implement parameter estimation and system identification methods for multiphase flow models Integrate experimental
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Months Rewards and Benefits: 33 days annual leave, plus 9 buildings closed days for all full time staff (Part time workers should pro rata this by their FTE). Use our total rewards calculator: https
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an interest in how psychological theory can improve synthetic data and in deepening our understanding of when and why LLM-generated responses approximate human behavior. The project involves a collaboration
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quite new within the field of computer vision. The neuromorphic design allows for a much higher acquisition frequency but most and foremost much longer acquisition time spans. This makes it ideal