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Field
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: foundation models, transformer architectures, graph neural networks, representation learning, or large-scale training Strong mathematical and algorithmic background, with the ability to design and analyse
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Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 14-Apr-26 Location: East Lansing, Michigan Type: Full-time Categories: Academic/Faculty Mathematics/Statistics
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collaborators across institutions to enable integration of scattering-derived insights into physics-informed AI models. Document research results, contribute to publications in peer-reviewed journals, and present
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21 Mar 2026 Job Information Organisation/Company CNRS Department Institut de biologie de l'Ecole Normale Supérieure Research Field Mathematics Researcher Profile First Stage Researcher (R1
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of Immunity – Artificial Intelligence (CoI-AI) Programme, a pioneering initiative that integrates controlled human infection models, advanced immunology, and AI-driven analytics to accelerate vaccine discovery
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Details Title Postdoctoral research fellows in generative, multimodal AI, and seismic foundational models School Faculty of Arts and Sciences Department/Area Earth and Planetary Sciences Position
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methods and research software empower modern biomedical research. Your mission As a Postdoc you will help build the next generation of multimodal and explainable AI models for personalized risk prediction
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field (e.g., engineering, applied mathematics, statistics) Strong theoretical understanding and practical experience in machine learning, multimodal foundation models, continuous learning, and agentic AI
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of clonal hematopoiesis in the elderly. The position is funded for 10 months from a UKRI FLF grant and aims to implement AI based risk assessment models of clonal hematopoiesis utilizing evolutionary models
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applications. A reverse modeling strategy will be used to design an optimum test matrix. This framework will enable modeling and predicting ion-irradiated mechanical properties using reduced test data, thereby