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bioinformatics for immunology research programs. You'll work at the cutting edge of AI-enhanced immunology, applying deep learning, foundation models, and advanced machine learning approaches to understand how
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Learning Sciences, Computer Science, Human-Computer Interaction, Informatics, Educational Measurement and Statistics, Educational Psychology, or a related field. Required Qualifications: Relevant experience
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next-generation machine learning (ML) models that are both data-efficient and transferable, enabling more reliable catastrophic risk prediction, defined as the probability of exceeding critical safety
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | 19 days ago
leverage machine learning techniques to bypass IO bottlenecks in the context of physics simulation on high-performance computing (HPC) clusters. This work is thus placed in a broader ``Machine Learning for
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Date: August 15, 2026 (negotiable) Specialty Areas: Computational Linguistics, Language Acquisition, Cognitive Modeling, Machine Learning The Department of Linguistics at the University of Michigan
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: Prior experience running behavioral experiments is desirable, as is previous collaboration or engagement with researchers in economics. Familiarity with methods from machine learning will be a plus. All
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, implementation, and analysis of machine learning models for computer vision tasks (40%). Analysis of natural scene statistics in aquatic and terrestrial environments (40%). Design of models to learn texture
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distributed wireless systems" which is conducted in collaboration between Linköping University (LiU) and Lund University (LU). Read more here: https://elliit.se/project/machine-learning-for-sensing-in
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education/experience) Required License No If yes, what is the required licensure/certification? Required Computer Applications: Microsoft Excel, Microsoft PowerPoint, Microsoft Outlook Required Other Computer
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project involves interdisciplinary research at the interface of computer science and mathematics, with a focus on bivariate molecular machine learning for modeling molecular interactions and properties