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Field
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trials, multiple endpoints, adaptive designs, Bayesian design and analysis methods, estimands, meta-analyses, benefit-risk analyses, subgroup analyses, biosimilars, patient experience data, bioequivalence
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such as Bayesian modelling and optimal control theory. Using state-of-the-art methods – including virtual reality, wearable sensing, motion platforms and advanced data analytics – you will place particular
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Assistant Professor positions beginning August 16, 2027. We seek outstanding candidates with research interests in causal inference, Bayesian computation, statistics, machine learning, or related areas
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statistical modelling, particularly Bayesian and/or hierarchical models A track record of scientific writing, demonstrated through theses, manuscripts, reports, publications, or other research outputs
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genomic or molecular datasets Background in Bayesian statistics, causal inference, or probabilistic modelling. Familiarity with modern AI architectures and foundation models. Organizational and social
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proficiency in oral and written English, creativity, thoroughness, and a structured approach to problem-solving Additional qualifications Experience with one or more of the following areas is meriting: Bayesian
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. Application of Bayesian mixing models to investigate consumer diets and food web pathways Requirements To meet the general entry requirements you must have been awarded a second-cycle (Master’s) qualification
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mathematical background Core skills: Probability and statistics. Estimation, Bayesian inference, uncertainty quantification and calibration (proper scoring rules, reliability diagrams, ECE), experiment design
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Bayesian inference, likelihood-free inference, uncertainty quantification, identifiability analysis, or scientific machine learning. Strong programming skills (Python, Julia, Matlab, C++, or similar). Strong
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. The practical element of your project will be based on, but not limited to, time series analysis, network analysis, Bayesian inference, Machine Learning, as well as computational simulation of mathematical models