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Field
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powerful ideas and tools at the intersection of topological band theory, symmetry analysis, and photonics. You will work on developing and applying these ideas to discover new topological phenomena, design
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to produce resilient and high-performing models. · PhD in Computer Science, Machine Learning, Mathematics, Physics, Statistics, or a related field Strong track record of applying ML in academic or industry
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, Materials Sciences , materials theory , materials theory; , materials-related physics , Mathematical Physics , Matter Theory , Mechanical Engineering , Medical Dosimetrist , Medical Physics , Mesoscopic
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/achieve a minimum of a merit at master’s degree level (or international equivalent) in a science, mathematics or engineering discipline. Applicants without a master's qualification may be considered
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, including electrical engineering, control theory, industrial engineering, electronics engineering, energy policy, data science, and applied mathematics. As part of the Alliance program, your project will be
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Biology, Physics, Applied Mathematics, Computer Science, Bioengineering, Systems Biology or a related field. Proficiency in modelling using differential equations is required. Candidates must have
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, with a background in Engineering, Mathematics or a related discipline. Depending on experience and academic background, successful candidates should have experience in one or more of the following
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effort at the intersection of machine learning and applied mechanics. The focus of this position is on extracting information about what a neural network has learnt in a symbolic and (human) interpretable
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, classical ecological theories and methods to mathematically model species interactions networks and communities across spatial scales. As part of the project, the student will extend these methods and make
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psychology A successfully completed scientific university degree in psychology Accurate working and strong team spirit Confident handling of large, longitudinal data sets Competent use of statistics and data