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SUSMAT-RC - Postdoc Position in Computer-Aided Design and Discovery of Sustainable Polymer Materials
candidate will work on an exciting project focused on extracting and analyzing experimental and computational data to develop predictive models for polymer-based materials. This project aims to leverage
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phenomena at stake and the accurate prediction of such complex multi-phase, multi-physics system is then necessary to optimize the system parameters entirely. The first step consists in designing and
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patient-specific, predictive models using real-world clinical data. The project aims to enhance understanding of disease trajectories, optimize treatment strategies, and support real-time clinical decision
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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required. Perform other duties as assigned to meet the goals and objectives of the department and institution. Maintains regular and predictable attendance. Minimum Education and/or Training: Ph.D. degree
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) and Data Science (4th science) towards the new “Science of Prediction” as the 5th scientific paradigm. Prediction and control have long been studied, but it is still a challenge to apply the traditional
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primarily research on Reinforcement Learning, and/or Optimal Control, and/or Model Predictive Control. RISC invites qualified applicants in the areas of electrical, computer, or mechanical engineering, or
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Abstract: This position focuses on the development of intelligent and autonomous drone systems, integrating AI, edge computing, and digital twin technologies for mission autonomy and predictive
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at Linköping University, where we laid the foundation for recent breakthroughs in protein structure prediction, which was later awarded the 2024 Nobel Prize in Chemistry. Since then, we have further developed
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partner from data sciences provides data management and AI based Image analysis, an internal simulations group working on quantitative models to reproduce and predict experimental data, and an internal