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Are you inspired by the quest for new materials for solar cells, spintronics, and quantum technologies—and eager to accelerate their discovery with machine learning and materials theory? Are you
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machine learning to facilitate crop breeding by design. This project envisions to build a system that enables precise introgression of desirable traits into elite crop varieties by predicting recombination
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technologies—and eager to accelerate their discovery with machine learning and materials theory? Are you passionate about linking atomistic processes to device performance through computer simulations? Are you
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language models, deep learning, and information retrieval? The Information Retrieval Lab (IRLab) at the University of Amsterdam is looking for a postdoc researcher to join our research team and contribute
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at RIBES using machine learning and genomics to overcome linkage drag and accelerate crop breeding by design. You will build models, analyse new sequencing data and optimise strategies for combining
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Are you interested in cutting-edge research at the intersection of large language models, deep learning, and information retrieval? The Information Retrieval Lab (IRLab) at the University
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wide range of resources and is mostly not publicly available. While sharing proprietary data to train machine learning models is not an option, training models on multiple distributed data sources
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”. Are you excited about the opportunities that data science in nutrition—especially the role of targeted metabolic modelling, machine learning and AI offer in developing effective personalized nutrition
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the execution of the dietary intervention trials Ability to apply machine learning, statistical techniques, and AI to analyze data, predict response to diet, and identify signatures determining response
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) for engineering systems and structures, as well as expertise in machine learning, stochastic modeling, and Bayesian statistics. Programming Skills: Proficiency in programming languages such as Python, C, or R