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equivalent foreign degree, obtained within the last three years prior to the application deadline Experience with simulation frameworks, system-level performance evaluation, or machine learning, is highly
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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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with national and international teams of mathematicians and computer scientists PhD applicants must possess a Master's degree in mathematics, theoretical physics, computer science, or a related field
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models
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for Transportation (VITA ) is looking for a postdoctoral researcher in the area of Generative AI. VITA research interests lie at the intersection of Computer Vision, Machine Learning (Deep Learning), and Human-Robot
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, bioelectrical signalling and fibrotic cell-state transition. More information on the project can be found here: https://marcfernandezyague.com The successful candidate will conduct experiments at the Queen Mary
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. The postdoctoral scholar will contribute to an exciting research program within the Foy Lab (www.foylab.xyz/) https://foylab.xyz/ , developing machine learning, computational, and mathematical models for improving
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: Conceptualisation and synthesis of data integration and visualisation workflows. Data management and the development of knowledge graphs Development and application of AI and machine learning methods and pipelines
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depth in some of the following areas (not all are required): Large-scale data analysis and learning analytics methods Experimental or quasi-experimental design; validity and measurement Working with LLMs
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position within a Research Infrastructure? No Offer Description Activities and context: The fellow will develop machine-learning interatomic potentials (MLPs), trained on density functional theory-DFT data