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Field
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related discipline, with a focus on security, privacy, or applied machine learning, and with less than 5 years post receipt of PhD. Demonstrated research experience in at least one of the lab’s research
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the ambition to grow. For more information about LIACS, visit our website. What you bring We are looking for candidates with: A PhD in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision
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. Responsibilities: Conduct research on automated reasoning and proof checker, AI-assisted collaboration. Develop and analyze algorithms for learning and optimization. Participate in collaborative research projects
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packaging and e-waste, while Exergy will develop the digital-twin and machine-learning tools that make the process adaptive and scalable. The research effort will be directed towards the selective recovery in
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: experience working with spatial omics or spatially resolved data in tissue experience applying AI or machine learning methods to digital pathology or histology data experience analysing kidney or transplant
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genetically diverse mice under a multitude of conditions, and (3) develop multimodal machine learning models and methods to determine signatures and biomarkers to understand mechanisms distinguishing
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Intelligence and Machine Learning. Join a team of scientists at the leading macromolecular crystallography beamlines, the crystallization laboratory, the computing center and contribute to science projects
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, or be nearing completion of, a PhD in Computer Science or a cognate field. The successful candidate must possess expertise in distributed AI/ML systems and computer networks. Good knowledge and practical
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-scale, diverse datasets using machine learning and AI techniques. Findings will be disseminated through peer-reviewed publications, conference presentations, and public engagement. About You You will hold
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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | about 8 hours ago
stochastic methods. Build 3D geologic and petrophysical models of oil and gas reservoirs using well logs, core, seismic, and completion data. Integrate machine-learning and data-driven techniques for reservoir