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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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, 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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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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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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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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University of Massachusetts Medical School | Worcester, Massachusetts | United States | 42 minutes ago
modeling, or machine learning - Experience with large-scale genomic data analysis (e.g., GWAS, QTL, PRS, or multi-omics integration) Strong programming skills in R or Python; familiarity with Bayesian
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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
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EU MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of
between process parameters and material properties will be developed and subsequently exposed to Bayesian optimization to find the optimal set of parameters that improve process performance and material
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require the use of cognitive shortcuts. - To develop and fit computational models (e.g., reinforcement learning, Bayesian models) to participant data, allowing for a precise, quantitative definition of