172 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" positions at University of Birmingham
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records that can systematically inform preparedness, training and future response. As a result, learning from past events is fragmented, inconsistently captured, and insufficiently embedded into emergency
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data set (e.g. neutron irradiations, that take years/decades to generate). Digilab brings AI/ML (artificial intelligence / machine learning) approaches for data engineering and automation to utilise
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. Experience in coding (e.g., Python/R/Matlab) and experience in behavioural experimentation, statistics, or machine learning is desirable but full training will be provided. Interviews for this studentship
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. The PhD will combine behavioural experiments, machine learning, and explainable-AI methods to answer questions: Do SR techniques improve human face identification accuracy? How do SR-enhanced images affect
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systems and tools to respond using an ‘all hazards’ approach. For more information, please see: https://www.birmingham.ac.uk/research/centres-institutes/nihr-health-protection-research-unit-in-emergency
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applicable across emergency preparedness and response settings. For more information, please see: https://www.birmingham.ac.uk/research/centres-institutes/nihr-health-protection-research-unit-in-emergency
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testing, flight dynamics and control. How to apply: The application will be made through the university’s online application system (https://sits.bham.ac.uk/lpages/EPS024.htm). Please provide a cover letter
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information, please see here: https://www.birmingham.ac.uk/research/centres-institutes/nihr-health-protection-research-unit-in-emergency-preparedness-and-response Supervision team: Dmitri Nepogodiev (Associate
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team (https://www.cancergrandchallenges.org/rewire-can ). REWIRE-CAN will explore how novel signalling activators can be used to directly kill cancer cells (hyperactivation lethality) and/or transition
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for preparing meetings related to the expertise area * *To also be assessed via interview How to apply Candidates should submit applications via https://apply.kcl.ac.uk and select the History Research MPhil/PhD