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science, data science, applied mathematics, physics, materials science, or a related field. Solid background in machine learning and/or computer vision. Interest in representation learning, active learning
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PhD program. This is the eleventh in a yearly recurring call. These positions are special in the sense that most PhD student positions at Chalmers are announced by a specific research group, funded by a
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State University of New York University at Albany | Albany, New York | United States | about 20 hours ago
, AI, and quantum information processing. Requirements: Minimum Qualifications: A terminal doctoral degree (PhD or equivalent) in physics, computer engineering, or related field from a college or
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Expert Demonstrations and Self-Experience” (https://elliit.se/ ). As a PhD student, you devote most of your time to doctoral studies and the research projects of which you are part. Your work may also
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lifting, pushing, or pulling of objects up to 50 pounds and loading and/or unloading supplies and materials onto carts. Sitting at a computer workstation for extended periods of time and repetitive motion
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applicants in the areas of Artificial Intelligence (including Generative AI & LLM, Machine Learning, Natural Language Processing, Computer Vision, and Knowledge Engineering), Data Mining and Analytics
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. Experience: Postdoctoral experience. Experience working in higher education, academic, or industrial research environment. Technical Knowledge or Skills: Knowledge of basic computer proficiency including
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covers areas such as pure mathematics, applied mathematics, mathematical statistics, as well as computer vision and machine learning. The department has approximately 150 employees, including 21 full
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Ecole Nationale des Ponts et Chaussées (ENPC) | Champs sur Marne, le de France | France | 21 days ago
-018-9301-4 Guayacán-Carrillo & Sulem, 2024. Comp & Geotech, 171, 106355. https://doi.org/10.1016/j.compgeo.2024.106355 Orgogozo et al., 2023. Computer Physics Communications, 282, 108541, https
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., Mendoza, M., Vetter, R., & Herrmann, H. J. (2016). Particle-Fluid-Structure Interaction for Debris Flow Impact on Flexible Barriers. Computer-Aided Civil and Infrastructure Engineering, 31(5), 323–333