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Website https://www.academictransfer.com/en/jobs/358703/phd-in-scalable-safe-ai-for-sem… Requirements Specific Requirements A master’s degree AI, Machine Learning, Data Science, Computer Science or a
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to develop new methods, for example using machine learning. have a proven track record of independent research funding and high quality publications. have at least 5 years of post-PhD work experience
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Artificial Intelligence (AI) and Machine Learning (ML). In this position students will contribute to research projects in CKL and as part of their education, will also engage in a dedicated 6-months internship
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research. You will strengthen the data science and machine learning activities of IAS-9 by developing core AI methods with applications to electron microscopy and materials discovery. You will work in a team
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astrophysics (completed by the start date), demonstrated experience in large-scale structure simulations, working knowledge of applications of machine learning techniques in cosmology and/or astrophysics (in
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therapeutics, wearables, artificial intelligence (AI) and machine learning (ML), public health surveillance systems, and virtual/augmented/extended reality. Health conditions of interest are also broad and may
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flexibility orchestration Scalable data and machine learning pipelines Digital twin architectures for cyber-physical energy systems AI-based energy system modeling, simulation, and optimization Secure and
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workflows, including adaptive, automated, or agent-based (agentic) workflows that integrate simulation, data analysis, and/or machine learning. Experience with computational workflows on large-scale HPC
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information science, data science, cybersecurity, game design, and immersive technologies. Established in 2023 and expanded in 2025, the college provides STEM-designated undergraduate and master’s degrees, and a PhD
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numerical models and machine learning tools to predict loads, assess structural responses, and identify damage under extreme conditions. By combining computational simulations with data-driven approaches