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information ● Experience with statistical methods including multivariate statistics, Bayesian statistics and linear/non-linear mixed-effects models ● Proficiency in the programming languages R
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, including reinforcement learning, hierarchical models, Bayesian inference etc. More details of key responsibilities of this role, in addition to the essential and desirable job criteria, are available in
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Multiple Research-Intensive Associate/Full Professor Tenure System Positions & an 1855 Professorship
, implementation science, geospatial analysis, biostatistics and research design, analysis of interventions (e.g., difference-in-differences), AI analytics, agent-based modeling, Bayesian modeling, causal inference
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multivariate statistics, dimensionality reduction, and latent variable modeling. Experience with temporal or dynamical modeling, Bayesian inference, and survival analysis for clinical outcome data. Scholarly
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finite-temperature anharmonic effects, thereby expanding the existing ab initio thermodynamic database to support the Bayesian inversion framework of the SHARP Thematic Project (Task 21, WP3). The project
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. This should include expertise in linear-mixed and structural-equation frameworks. Expertise beyond those domains is very welcome, including Bayesian and machine-learning. Preference will be for individuals who
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of multi-modal healthcare record data. The ideal candidate will additionally have experience: Multi-modal AI model development Statistical modelling techniques (Bayesian inference, differential equations and
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Mathematics, and Statistics mirroring the School’s strong research programs. It has particular strengths in Algebra, Analysis, Combinatorics, Geometry, Mathematical Physics and Number Theory, Bayesian and Monte
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. Funding Opportunities: Identifying and prioritizing grant and external funding opportunities that will offer the highest yield for the lab's objectives, including making decisions on when and how to pursue
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interdisciplinary collaboration. Faculty and students conduct innovative research in Bayesian methods, causal inference, data science, machine learning, statistical genetics, longitudinal and survival analysis, and