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in OP-MEG and Bayesian computations. This role offers you the chance to collaborate with leading researchers, mentor students, and shape the future of cognitive neuroscience through innovative research
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employment. Position description The successful candidate will work within the research project “Advances in generalized Bayesian inference via differential-geometric methods” funded by the Research Council
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learning, Large Language Models, Stochastic optimization, Transfer & Evolutionary optimization, Bayesian optimization for complex design in material and engineering. Key Responsibilities: Collect relevant
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, Bayesian inference and interrogation theory. The post may involve travel to Iceland and Italy in support of your work and attendance at international conferences, such as the European Geothermal Congress
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project developing Bayesian causal inference methods for mediation analysis using Electronic Health Records (EHR) data. The Research Fellow will design and implement Bayesian methods and software
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probability, partial differential equations, and mathematical physics. In statistics, these include biostatistics, optimal design, computer experiments, sequential analysis, shape-constrained inference, time
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data Clear scientific writing and communication; a track record of publications Experience with causal inference Bonus: experience with explainable ML, optimization/decision strategies, or work with EHR
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transferring learning from other geographic regions and data types, machine learning methods, Bayesian inference and interrogation theory. The post may involve travel to Iceland and Italy in support of your work
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data Clear scientific writing and communication; a track record of publications Experience with causal inference Bonus: experience with explainable ML, optimization/decision strategies, or work with EHR
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recruit and supporting the academic career development of postdoctoral fellows. As part of this program MCHB and HSPH are committed to establish a tenure track position in MCH during the 5-year funding