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Learning for a period of up to 24 months. The project, funded by EPSRC, will involve exploring the use of machine learning to develop new tools for investigating catalytic reaction mechanisms, with a focus
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the division of Data Science and AI , we develop data-driven methods and AI solutions that support intelligent decisions across society, advancing machine learning techniques, from foundations to industrial and
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Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to biological data collection, extraction, and standardization
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Lawrence Berkeley National Laboratory is hiring a Postdoctoral Researcher – Machine Learning for Materials Science within the Molecular Foundry division. Molecular Foundry is a Department of Energy
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methods with the ability to implement and evaluate machine-learning systems at scale. Candidates may come from topological data analysis, geometric deep learning, network science, statistical physics
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of engagement. Read more at: Department of Computing Science Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven
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machine learning with a physics-based understanding of the growth process. Doping and processing of ultra-wide bandgap semiconductors present challenges, but it can enable electronic devices with
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project focused on developing an advanced machine learning framework for spatio-temporal datasets. The position is for 2.5 years and is partially funded by the Dutch Research Council (NWO) through
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the intersection of machine learning (ML) and the sounds of wildlife (“bioacoustics”). We are also happy to consider candidates in one of the two fields who can demonstrate a strong basis for working in this cross
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Associate position focusing on control systems engineering, artificial intelligence (AI), and scientific machine learning (SciML) applied to nuclear fusion energy. The successful candidate will join the