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focusing on the integration of machine learning, wafer-scale synthesis of materials. The role will contribute to the university's research mission by conducting fundamental research, helping secure external
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part of the Structural Genomics Consortium (SGC) Target 2035 Initiative, a global collaboration in the area of protein science, machine learning and data science towards improving our ability to predict
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: Education: Ph.D. in machine learning, computer science, engineering, physical science or related technical discipline. Experience: Expertise in developing and training AI models Proficiency in Python
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Carnegie Mellon University, Institute for Computer-Aided Reasoning in Mathematics Position ID: 3637-PF [#27988] Position Title: Position Type: Postdoctoral Position Location: Pittsburgh
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computational chemistry, reaction network analysis, and machine learning for organometallic catalytic reactions. 2. Design of membrane-permeable macrocyclic peptide drugs via machine learning structure
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opportunity to field test and validate their methods using real-world systems. Postdoctoral fellows will work across the following research areas: Predictive machine learning Robust and stochastic optimization
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of 3D crystalline structures; – depending on the candidate's profile, implementing machine learning methods (AI & machine learning) for the analysis of physicochemical data from the hpmat.org database
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e.g., ultra-cold gases of bosonic or fermionic atoms, machine learning technologies and quantum computing. At the same time, we work in close connection with IJCLab experimentalists, particularly
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assimilation, machine learning, and seasonal weather forecasts. As a Postdoctoral Research Fellow, you will play a crucial role in developing and testing statistical models for the accurate forecasting
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[map ] Subject Areas: Mathematics; Physics; Astrophysics; High Performance Computing; Machine Learning Appl Deadline: 2025/12/16 04:59 AM UnitedKingdomTime (posted 2025/11/20 05:00 AM UnitedKingdomTime