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or equivalent experience in an appropriate discipline A background in numerical ice-sheet, glacier or fracture modelling, or equivalent experience Robust experience with computer coding (e.g., Python, C++) Track
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the appropriate area. Familiarity with statistical analysis software (e.g., STATA, R, SPSS) or computer programming (e.g. C++, Python, R) and experience working with health-related data will be advantageous
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the role Overview of the role We are seeking a highly motivated Research Fellow in Machine Learning to join the PharosAI team, focusing on developing novel machine learning methods in computer vision
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to appoint a Research Fellow to support world class research and development activities on SiGe epitaxial materials for realization of quantum computer processor. Quantum computers promise to be one
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of the repair economy in the UK through interaction with brands, manufacturers, policy-makers, waste processors and the public. About the role One of the key results of the Big Repair Project is that
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for implementing the model as a computer simulation and analysing it within a health-economics framework using standard computational techniques. The post-holder will also be responsible for writing up the findings
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. The Research Associate/Fellow will be a human computer interaction (HCI) specialist in qualitative (interview) data collection, analysis, and sensemaking. They should also have a broad understanding of
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application please include detail on how you match the person specification. Main Duties The responsibilities may include some but not all of the responsibilities outlined below. Developing novel computer-based
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epilepticus. You will work in the group of academic neurologist and computer scientist Dr Gregory Scott, with a team of collaborators having expertise in translational neuroscience, clinical neurology, signal
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Health Regulations. Assist in project resource requirements and managing student projects. Job Requirements: Have a PhD Degree in Computer Engineering/Computer Science/Electrical Engineering or equivalent