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-year Computational Project to use state-of-the-art Computational Chemistry techniques to understand structure-property relationships in thermal batteries, and to derive new understanding to guide design
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looking for curious, enthusiastic and hard-working candidates with the following expertise: -- an understanding of concepts in fluid mechanics, and analytical and numerical methods to solve partial
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systems are generally ill-conditioned. The project sits at the intersection of classical numerical analysis, scientific machine learning and computational chemistry. Based on regularization techniques and
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mechanics or quantum chemistry is required; prior experience with electronic-structure theory or numerical simulation methods is an advantage but not essential, as full training will be provided. How to apply
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Statistical methods are leveraged in many scientific applications to: specify a data collection practice (experimental design), draw conclusions from sparse and noisy data (inference), and assess
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. The research aligns with the objectives of the EU funded project DRAGONS (Direct Recycling with AI for Gigafactory Operations and Next-generation Sustainability) programme, which seeks to develop scalable and
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, sensor design and calibration, finite element modelling, polymer processing, embedded electronics, and ex vivo tissue methods. The University is uniquely positioned to benefit any applicant interested in a
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actuation, feedback and computation across their soft bodies, blurring the boundary between material and machine. Our work hints that key platforms to capture such material intelligence are ‘robotic materials
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fusion/fission reactors, to aerospace gas turbines and concentrated solar power. This involves the design of fundamentally new alloys by computational methods; production through arc melting, powder
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This PhD project will investigate partial differential equations arising from kinetic models, with a particular focus on variational methods. The research will explore how variational structures