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Field
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Bayes factor hypothesis tests in factorial designs. What are you going to do The envisioned projects will focus on the following activities related to Bayesian inference in factorial designs: Construction
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software engineering, Bayesian modeling and approaches to data analysis. Key Responsibilities: Preprocessing and data scientific approaches to analyzing human behavioral data Computational model development
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experience in one or more of: large-scale data analysis, time-series photometry, spectroscopy, astrometry, Bayesian/statistical inference, and/or software development for astronomical datasets. Department
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and experience in applying statistical methods to real-world data. Familiarity with Bayesian statistics and early phase adaptive trials are highly desirable. Effective oral and written communication
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PhD Studentship: LLM-Based Agentic AI: Foundations, Systems & Applications – PhD (University Funded)
of machine learning, uncertainty quantification, and Bayesian modelling. They will provide complementary expertise to bridge agentic AI with real-world impact. What We Are Looking from You Background in
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, seismology, geophysics or a relevant scientific field Have extensive experience in Bayesian statistics, mathematical optimisation and parallelisation of calculations Be familiar with ground motion analysis
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to address more novel problems. Keywords include: automatic experimental design, Bayesian inference, human-in-the-loop learning, machine teaching, privacy-preserving learning, reinforcement learning, inverse
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Requirements An ideal candidate will also have experience in modelling of insect dispersal and/or climatic suitability analysis and Bayesian statistics. Previous involvement in acquiring research funding will be
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or food safety risk assessment, toxicology and/or Bayesian statistics. Previous involvement in acquiring research funding will be considered as merit. The position requires excellent written and verbal
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sources, including developing a Bayesian Hierarchical Modeling framework; (2) using integrative modeling approaches to characterize heterogeneous protein assemblies structures and dynamics; (3) developing