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Field
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Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field of physics—ideally in accelerator science or engineering—or a closely related field Demonstrated experience or strong interest
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beyond the Standard Model, including effective field theories and perturbative QCD, phenomenology at current and future colliders, as well as emerging areas in Artificial Intelligence, Machine Learning
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Artificial Lighting Solution for Greenhouses”, we propose to design and synthesize sustainable, high- luminescent nanomaterials, specifically carbon dots, derived from plant-based resources, to replace non
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feed into this vision. The intended start date is July–August 2026. Job requirements PhD in machine learning, artificial intelligence, computational chemistry, computational materials science, or a
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to project applications and the acquisition of third-party funding, and help to manage projects; • Provide scientific and technical support to team members, FLEXPART co-developers, PhD and master students
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on a predictor X; candidates should understand how this function performs the analysis (e.g., estimates the effect size, assesses significance). Most likely, the applicants will have a BSC and/or PhD
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awareness. We welcome applications from recent PhD graduates in relevant areas, such as artificial intelligence and natural language processing. An ideal candidate will have a strong technical background in
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will carry out research activities and support the PhD students working on this project. This includes conducting literature surveys and establishing state-of-the-art; developing necessary experimental
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familiarity in machine learning (ML) and artificial intelligence (AI). This role is pivotal in evaluating the economic competitiveness of the U.S. in the production and manufacturing of energy-related materials
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management, workflow management, High Performance Computing (HPC), machine learning and Artificial Intelligence to enhance our capabilities in making AI-ready scientific data. As a postdoctoral fellow at ORNL