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mathematical models with state-of-the-art machine learning techniques to develop forecasting models that are more accurate, efficient, and capable of incorporating complex atmospheric phenomena. This is a fixed
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mathematical models with state-of-the-art machine learning techniques to develop forecasting models that are more accurate, efficient, and capable of incorporating complex atmospheric phenomena. This is a fixed
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to develop predictive approaches for future scenarios. The skills, qualifications and experience required: obtained (or close to obtaining) a PhD in a scientific, mathematical, engineering or computing subject
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academic department that encompasses computer science along with many aspects of engineering, technology and mathematics. We have a world wide reputation for academic research with consistent top research
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; experience with mathematical modelling and common convex optimization algorithms; experience with mathematical modelling for non-deterministic polynomial (NP-hard) problems; familiarity with common digital
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simulation software packages and finite element analysis; previous knowledge in wireless power transfer; experience in spatial magnetic field shaping; experience with mathematical modelling and common convex
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, preferably in the fields of Reactor Physics and Radiation Transport Modelling Methods. They should have a strong mathematical foundation in Monte Carlo methods, proven ability of their application
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Leverhulme Trust International Professorship. The position will be in the research cluster Fundamental Particle Physics in the Department of Mathematical Sciences which is part of the School of Physical
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or a related area, with extensive research experience and a strong publication record. Excellent mathematical and programming skills are essential, with experience in two or more of Bayesian methods
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or a related area, with extensive research experience and a strong publication record. Excellent mathematical and programming skills are essential, with experience in two or more of Bayesian methods