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together with relevant experience. You will have a strong technical background in machine learning, especially RL and LLMs. An ability to work independently and as part of a collaborative research team is
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on and defensive mechanisms for safe multi-agent systems, powered by LLM and VLM models. Candidates should possess a PhD (or be near completion) in Machine Learning or a highly related discispline. You
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information and advice on best-practice methodologies in machine learning/deep learning. It is essential that you hold a PhD/DPhil (or close to completion) in a relevant quantitative field (e.g. biostatistics
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research is essential, and experience working with electronic health records, microbiology, or machine learning would be very welcome. Applications from candidates who do not fulfil the essential criteria
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will provide guidance to less experienced members of the research group, including postdocs, research assistants, technicians, and PhD and project students. Key responsibilities: • Manage own
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to learn and develop new experimental, computational and wet-lab techniques and approaches. • Identify genetic reagents and design experiments that test circuit function. • Construct
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influence clinical practice. We welcome applications from candidates with following backgrounds: Candidates with strong experience in medical image analysis, machine learning (especially deep learning) and
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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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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