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Field
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position will contribute to the research programme of the recently founded "AI Hub in Generative Models", a research consortium funded by EPSRC. The goal of the programme is to do research in the area of
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modelling • A high fascination for technical/scientific problems of theoretical and numerical research • Experience in the field of numerical time-accurate flow simulation, preferably turbomachinery • Ability
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strong research capabilities with a deep understanding of trading to design, validate, backtest, and implement statistical and advanced machine learning models. Your work will span a range of initiatives
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with a background in cognitive psychology, data science or computer science and a willingness to develop skills in computational models of cognitive processes, statistical methods, and programming (R
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such as the NEPS. Potential research areas include (but are not limited to): Item response modeling of achievement tests Analysis of process data (e.g., response times) to enhance competence measurements
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, natural language understanding, and more. Your work will directly influence our trading strategies and decision-making processes. This is a unique opportunity to work at the intersection of cutting-edge
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materials (natural and synthetic fibers, yarns and fabrics) which are highly anisotropic and non-linear. Furthermore, the dynamics of high-speed manufacturing processes need to be included in the modelling
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will generate a wide range of contemporary high-resolution next-generation sequencing and array-based genomic and epigenomic datasets across large cohorts of human tumours and experimental models
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. Applicant must have (or expect to obtain shortly) a Ph.D. in Biological Sciences or related field. Experience with Neuroimmunology and/or Immunology is highly preferred. Proficiency in mouse models of disease
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the testing of newly devel-oped materials and the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission