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, systems biology, physics and machine learning. The project offers a unique opportunity to collaborate closely with experimental scientists and contribute to translational advances in synthetic biology and
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opportunity for an enthusiastic machine learning researcher to push the boundaries of multimodal-AI by developing new models that incorporate data across several modalities, including imaging, text, social and
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, multidisciplinary research team focused on advancing cancer care through cutting-edge computational and AI technologies. We develop innovative approaches that combine deep learning, computer vision, and
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to the project’s scope, such as mechanistic interpretability of LLMs, robustness verification of machine learning models, and conformal inference. Applicants should demonstrate scientific creativity, research
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for King’s Engineering. We offer undergraduate and postgraduate education, with a distinctive approach, combining traditional teaching methods with modern, project-based learning, catering for the needs of our
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verification of machine learning models, and conformal inference. Applicants should demonstrate scientific creativity, research independence, the capacity to support junior team members, and strong communication
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-based cameras (e.g. DVXplorer), and tactile/force sensors. 3. Strong background in computer vision and deep learning, with practical implementation experience. 4. Proficiency in programming with
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tools such as R, GraphPad Prism. 4. Familiarity with state-of-the-art Machine Learning techniques, with the ability to apply them to bioimage analysis. This includes practical experience with
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of atomistic modelling of ferroelectric materials 2. Experience in development and application of machine learned potentials * Please note that this is a PhD level role but candidates who have submitted
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undergraduate and postgraduate education, with a distinctive approach, combining traditional teaching methods with modern, project-based learning, catering for the needs of our students and the industries in