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opportunity for a highly motivated and skilled Research Associate/Assistant in statistics to join the EPSRC funded project PINCODE: Pooling INference and COmbining Distributions Exactly: A Bayesian Approach
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Exactly: A Bayesian Approach. The project aims to address the challenges in pooling inference, by developing and implementing either exact or asymptotically exact Monte Carlo algorithms in collaboration
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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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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 20 days ago
for individuals that are interested in pursuing a PhD in economics or finance. The chosen candidate will also gain valuable experience in the application of machine learning and Bayesian inference methods
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and manipulating complex data structures, Bayesian modeling, analyzing nested longitudinal data, and who are familiar with techniques for handling challenging data (e.g., highly non-normal distributions
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Large vision–language models (LVLMs) can describe driving scenes and support decisions [Li25], but they sometimes hallucinate objects, relations, or events that are not present [Liu24,Liu25]. In a
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of objects (”curved”, ”pink”, ”having eyes”, “being animate”, ”having agency”, or ones that are hard to name) that are crucial for our everyday functioning. This research is only possible now thanks to 1
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regarding argument structures (e.g., nested arguments) and types (e.g., blocking arguments), the lack of objective evaluation of argument quality using normative methods. Through this researcher position, we
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the health of the territory, - as well as interventions with farmers aiming to generate new knowledge on the links between food, agriculture, biodiversity and health. The objective of this position is to
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objectives are met efficiently. Qualifications: A Ph.D. in Materials Science, Environmental Engineering or a related field. Proven experience in synthesis and characterization of nanomaterials or composites