18 bayesian-inference-"Integreat--Norwegian-Centre-for-Knowledge-driven-Machine-Learning" Fellowship positions in United States
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- Indiana University
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computational modeling, geometric morphometrics, multivariate and Bayesian statistics, spatiotemporal and spatial modeling (including GIS), causal inference, machine learning, AI, and statistical software
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architecture design, simulations, and publicly available genomic datasets to develop new inference methods. The Postdoctoral Associate will conduct research related to creating or testing deep learning models
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integrates custom neural architecture design, simulations, and publicly available genomic datasets to develop new inference methods. The Postdoctoral Associate will conduct research related to creating
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: integration of diverse datasets, robust statistical inference, and causal inference through statistical analysis, and strong team coordination capacities. A PhD in Geography, Environmental Science or Studies
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cancer: insights into tumour immunogenicity and immune evasion. Nat Rev Cancer 1–15 (2021) doi:10.1038/s41568-021-00339-z. Chen, J. et al. In silico tools for accurate HLA and KIR inference from clinical
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value and accessibility; Leveraging specialist expertise (NLP, network analysis, web scraping) to develop, evaluate, and deploy novel solutions over large datasets; Drawing substantive inferences from
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Language Model (LLM) Strong biostatistics knowledge including survival analysis and causal inference Experience with reinforcement learning, agentic AI systems and autonomous decision-making frameworks Data
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designs and methods, clinical trial methods, Bayesian methods, and developing R packages and scalable algorithms. Opportunities for collaboration across the Department of Biostatistics and the Medical
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Skip to content HARVARD.EDU About Mission / Vision People Annual Reports Contact Us Programs AWS Impact Computing Bias² Causal Inference CrisisReady Fellowships & Funds SPUDS Trust in Science See
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expertise in machine learning and/or Bayesian models is preferred. This position will involve both methodology development and analysis of multi-omic sequencing data, including spatial transcriptomic data