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to sustainability, and (v) energy efficiency in computing systems. Field of subject for the position: Computer Science with specialization in Machine Learning Placement: Department of Computer Science and Media
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Postdoc position (f_m_x) ,,Combining Physics-Based Machine Learning and Global Sensitivity Analys...
“Geosystems”), we are looking for a: Reference Number 10337 Are you seeking a PostDoc project at the interface between geoscience, machine learning and mathematics – with an application to the highly relevant
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Responsibilities The objective of this post is to offer interdisciplinary training to individuals interested in utilizing cognitive neuroscience and machine learning techniques to study learning, cognitive
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on the development and application of machine learning algorithms in areas such as surrogate modeling for physical systems, data assimilation, and scientific data reduction. The position comes with a travel allowance
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Postdoc (f/m/d): Machine Learning for Materials Modeling / Completed university studies (PhD) in ...
Area of research: Scientific / postdoctoral posts Starting date: 01.07.2025 Job description: Postdoc (f/m/d): Machine Learning for Materials Modeling With cutting-edge research in the fields
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The Mathematics and Computer Science Division (MCS) at Argonne National Laboratory is seeking a Postdoctoral Appointee to conduct cutting-edge research in scientific machine learning, focusing
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Postdoc in Machine Learning for Oligopeptide Design (1.0 FTE) (V24.0584) « Back to the overview Job description The advent of modern machine learning (ML) methodology is accelerating scientific
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We are seeking a full-time Postdoctoral Research Assistant in Machine Learning to join Torr Vision Group at the Department of Engineering Science (central Oxford). The post is funded by EPSRC and
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: Postdoctoral Position Location: Taipei, Taipei, Taiwan Subject Areas: Physics / Biology , Biophysics , Chemistry , Computational physics , Condensed Matter Physics , Machine Learning , Nanotechnology
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The Network Analysis and Modelling group investigates how genetic variation shapes gene regulation, protein function, and, ultimately, observable plant traits. Using machine learning and network