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working on nanotechnology, biosensing, optics, and machine learning to develop an advanced point-of-care diagnostic platform with a strong translational emphasis. In this new role, we are looking
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programming), (2) populating agent-based models with realistic agent behaviours (e.g. using machine learning techniques), (3) calibrating large-scale agent-based models and (4) validation and verification
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of original machine-learning based algorithms and models for multi-modal ultrasound guidance that are intuitive for a non-specialist to use while scanning and trustworthy. You will work with clinical domain
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Postdoctoral Research Associate in Forest Resilience, Climate Change, and Human Health in the Amazon
illnesses. The post holder will also co-supervise a PhD student who will be involved in the same project. This is a highly interdisciplinary project combining forest ecology, remote sensing, machine learning
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and leading a programme of numerical simulations relating to all aspects of our research on P-MoPAs; using particle-in-cell computer codes hosted on local and national high-performance computing
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making sure these are executed in a timely fashion and to deadlines. • Be willing to learn and develop new computational and wet-lab techniques and approaches required for the project as it evolves
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dissemination and grant writing. About you You will hold a PhD (or be close to completion) in a relevant field, in addition to experience of implementing or fine-tuning LLMs using machine learning libraries
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certification in data science, machine learning, or analytics, expertise in immunohistochemistry and digital imaging techniques. Previous experience working in a molecular or biochemistry laboratory and/or prior
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Love working with your hands? Keen to learn a real trade, earn while you learn, and be part of a team helping to maintain historic Oxford spaces? The University of Oxford is offering a 24-month
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management and analysis of EHRs, a background in data science and machine/deep learning, and solid programming skills in Python and/or R. The program has already collected large clinical data sets-we are now