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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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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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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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to start later for excellent candidates. The closing date is the 30th November 2027. Research topics include: Designing next-generation formal reasoning mechanisms by combining machine learning (especially
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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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are looking for candidates to have the following skills and experience: Essential criteria 1. Engineering or Science degree (MSc. or PhD) in applied mathematics, physics, biophysics, computer vision
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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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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
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synthesised and analysed to research and address slavery in war? By working across the Centre’s datasets and using data analytics and machine learning, this Centre strand will build a blended data resource that
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-quality robotics research in the areas of robot grasping and manipulation, kinematics and mechanisms, sensing, and human-robot interaction. Within CORE, SAIR focuses on multimodal machine learning for human