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to problem solve and work independently Desirable criteria Familiarity with the JASMIN data analysis facility Expertise in the hazard of extreme heat Competence in statistical analysis and/or machine learning
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, Computer Science or related numerate discipline. PhD or equivalent in at least one of the following subjects: Computer Science, Machine Learning, Biomedical Engineering, Physics, Applied Mathematics or other
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Job description The Department of Informatics is looking to appoint a Research Associate in Trustworthy Machine Learning for Malware Detection, to work on the EPSRC project XAdv: Robust Explanations
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for. You should apply if We are seeking an enthusiastic and dedicated researcher with a strong track record in data analyses and a passion for machine learning and artificial intelligence in healthcare. You
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of wellbeing and social justice, perhaps using machine learning, digital technology and advanced modelling skills. The successful candidate will have a PhD in a relevant development studies discipline and will
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Iacoangeli lead). The postholder will develop bioinformatics and machine learning methods to analyze large multi-omics datasets of patients affected by MND and controls. There will be a specific focus on the
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the use of imaging analysis software (Image J, QPath, Imaris, MATLAB, machine learning) Expertise in bioengineering, biomaterials * Please note that this is a PhD level role but candidates who have
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designing machine learning algorithms for edge-enabled wireless networks is preferred. At King’s, you will join a research-leading and multi-disciplinary team REASON, including Dr Yansha Deng, Dr Vasilis
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these projects To engage with industrial and academic partners to apply novel mathematical, statistical and machine learning and artificial intelligence techniques to relevant problems To disseminate the research
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innovative research in machine learning, reinforcement learning and multi-agent reinforcement learning. 5. Ability to use Python (or any other programming languages) with strong coding skills. 6