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Field
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modelling to study the causes and consequences of extreme chromosomal instability in these cancers. The role will involve: - Learning and applying cytogenetic methods for generation and analysis of chromosome
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specialised standard operating procedures. Additionally, candidates should possess expertise in host-pathogen interactions, supported by relevant experimental approaches, models, and analytical techniques
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Science, Robotics, AI, or a related field Strong background in machine learning and robotics, with specialisation in one or more of the following areas: generative models, reinforcement learning, human-centred AI
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Science, Robotics, AI, or a related field 2. Strong background in machine learning and robotics, with specialisation in one or more of the following areas: generative models, reinforcement learning, human
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About the Role We have been funded by Alzheimer’s Society led by Prof Nathan Davies to evaluate hospital at home services/care models for people living with dementia. The role will lead the study
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responsibilities will include: Pre-registering data analysis plans; Leading and conducting advanced statistical analyses (e.g., twin/family designs, genomic and epidemiological methods, longitudinal modelling
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infrastructure perspective: short-term projects helping researchers with specific tasks, so that the researchers gain competence to work independently. Provide good role models of best open science practices. As
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expected. Excellent knowledge in dealing with complex statistical models and methods and the willingness to support the team in statistical questions are also expected. The candidate should have teaching
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fluorescence-lifetime detection (Fast-FLIM) and temporal focusing. This instrument will deliver quantitative, sub-second imaging of live three-dimensional cell-culture and organoid models, advancing fundamental
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better position. A model is needed into which to feed critical information and retrieve cause/effect insights on which to base logical decisions. Biological information cuts across diagnostic boundaries