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, United States of America [map ] Subject Areas: Statistics, Machine Learning, and AI Appl Deadline: 2025/07/01 11:59PM (posted 2024/10/31, listed until 2025/07/01) Position Description: Position Description The University
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Postdoctoral Researcher in Machine Learning of Isomerization in Porous Molecular Framework Materials
broad range of applications. Computational chemistry and Machine Learning increasingly underlies MFM research to search or screen candidate MFMs prior to synthesis. A major drawback when applying
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, at the University of Cambridge, UK. The Research Assistant will work together with a team of students and research collaborators on the development of learning-based control policies that facilitate
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following position Postdoctoral researcher (m/f/d) in Environmental Data Science and Machine Learning for the project BoTiKI Location: Görlitz Employment scope: full-time (40 weekly working hours) / part
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Pneumatic Tires, Structure-Process-Properties Relationships. How will you contribute? Do you have proven skills in data analysis, machine learning, as well as in mathematical and computational modelling? You
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postdoctoral researcher for pioneering research at the convergence of artificial intelligence (AI) and materials science. The ideal candidate should possess expertise in scientific machine learning (SciML
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We are seeking a full-time Postdoctoral Research Assistant in Machine Learning to join the Visual Geometry Group (VGG), in central Oxford. The post is funded by Toyota Motor Europe and is fixed-term
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microscopy data analysis, chemometrics, and machine learning. This position is ideal for a researcher who enjoys working at the interface of imaging, data science, and environmental monitoring. The project
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evaluations, attacks on and defensive mechanisms for safe multi-agent systems, powered by LLM and VLM models. Candidates should possess a PhD (or be near completion) in Machine Learning or a highly related
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description The postdoctoral project is focused on development and the exploitation of machine learning tools to accelerate the analysis of microtomography data at the MAXIV synchrotron facility. MAXIV