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approaches combining control, learning, and uncertainty quantification. This project develops a data-driven control framework grounded in first-principles models with emphasis on: Data-driven practical
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. Duties SciLifeLab is now recruiting Data Engineers to integrate AI within SciLifeLab’s ecosystem of data and IT services for data-driven research. The role involves developing and maintaining systems and
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programmability, network exposure APIs, service orchestration, O‑RAN, network slicing, and data-driven optimization. You have proven experience with AI/ML pipelines, including data processing, model training
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Design to help build and translate AI‑driven capabilities for biologics design and protein engineering. The role combines protein structural insight with hands‑on ML development: adapting and applying
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Research Associates/Assistants to support a diverse portfolio of research projects at the intersection of infectious disease modelling, Bayesian inference, AI, and public health. Projects span AI-driven
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Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathemat
To strengthen our team in the division 7.7 “Modeling and Simulation” in Berlin-Steglitz, starting 01.07.2026, we are looking for a Research Assistant (m/f/d) with a Ph.D. in Civil Engineering
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experimentation, numerical modeling, and data-driven approaches to address complex environmental challenges. The role requires a high level of scientific creativity, independence, and leadership, with expectations
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» Modelling tools Engineering » Chemical engineering Researcher Profile First Stage Researcher (R1) Application Deadline 29 May 2026 - 23:59 (UTC) Country Belgium Type of Contract To be defined Job Status Full
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-design and evaluate data-driven solutions. Support technology adoption among agricultural operations consistent with land-grant mission. Teaching & Workforce Development Develop and teach undergraduate and
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(or equivalent) in data science, applied mathematics, electrical engineering, or a related field. • Strong background in machine learning and/or statistical modeling. • Good written and oral communication skills