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to the training and deployment of cutting-edge AI models. To support this exciting new centre, we are seeking a Research Statistician to play a critical role in data analysis and validation using appropriate
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. Research in the department is distributed across five main themes: Machine Intelligence Data Science Smart Infrastructure & Cyber Security Human‐Centred Computing Computational Modelling Role You will be a
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growing team committed to creating positive change through health psychology and behavioural science. The NIHR project offers the opportunity to collaborate with leading researchers from Sheffield Hallam
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Development. Application requirements: Interested in digital modelling, fashion and sustainability Knowledge of 3D modelling, high to low poly workflow, retopology, UV mapping, material design, texturing, real
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methods and landscape technologies Heritage landscapes Some previous teaching and academic experience is essential to meet the requirements of a Lecturer position. You will also have exceptional inter
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academically challenging, we also place great importance on experimentation, creativity and developing discipline-appropriate approaches to research. Manchester School of Art is ranked second in the UK for
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-positivist approaches to IR theory, who can support our students in developing nuanced, theoretically grounded arguments and confidence in their intellectual positions. Key Responsibilities: Lead and deliver
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” for asset tracking, performance monitoring, and reporting. Key Responsibilities Asset Database Management: Maintain an accurate and comprehensive database of mechanical, electrical, and building fabric assets
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should be eligible for Chartered Membership of the British Psychological Society and hold a PhD in Educational Psychology or a closely related field. To apply: For an informal discussion regarding
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outstanding research and excellent education. Much more than a university, we're a place where ambitious people unite as a diverse and inclusive community to make meaningful connections. Where world-class