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assessment models, and statistical modeling in both frequentist and Bayesian frameworks • A solid track record of publications Appointment Type Restricted Salary Information Commensuate with experience Review
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MOFs and their hybrid materials. The primary objective is to explore the fundamental structure–property relationships that govern MOF vitrification and liquid-phase behavior, and to develop new MOF-based
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of CRSA’s primary objectives is to provide decision- support tools and services to local, national, and international users in water and food systems. Job Description: Over the past few years, advances in
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 4 days ago
the objective of simulating the complete ECT problem, 2 main challenges have to be faced, and will be investigated during the post-doc. First, the 3D meshing challenge. The difficulties include embedded
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(CBS) is an entity of the Mohammed VI Polytechnic University (UM6P). The main objective of CBS is to set up a distinctive research-teaching program of international level, in order to meet the research
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: We are excited to announce a Postdoctoral Researcher position in the group of Prof. Gunda Köllensperger. The major objective is to downscale metabolomic techniques to the level of organelles, small
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candidate will carry out duties to the highest standard and will: Undertake original research of international excellence. Develop research objectives and proposals for own and/or collaborative research
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with statistical modeling (ideally Bayesian statistics) • Proficiency in Fortran, R, Python, Matlab, or ideally other common languages (e.g., C/C++) Strong computational skills Strong oral and written
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Generation in Context: Object- and Interaction-Aware Generative AI Models of Language Use”. Preferred start date is 1 February 2026 but is negotiable. Job descriptionFace-to-face conversation is the primary
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on the training strategies. In this project, we will investigate Bayesian methods to train deterministic SNNs (with deterministic activation functions) or probabilistic SNNs. Bayesian deep learning methods have