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chemistry, from the use of advanced electronic structure methods to the development of dynamical approaches to study photochemical reactions, also including machine learning. The group is part of the Cluster
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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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and early-onset cases without a known genetic cause. We are also interested in genetic interactions (epistasis), tandem repeats, machine learning, and other areas of AD research that have not yet been
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in the United States. Preferred methodological skills include statistical analysis of survey and other large-n data, qualitative interviews, and/or text analysis and machine learning skills
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of the ERC Consolidator project AUTOMATIX (see details below), we are seeking a PhD candidate to develop machine learning approaches for constitutive modeling. Context With the advent of machine-learning (ML
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workflows that integrate modern AI and machine learning concepts (e.g., surrogate models, adaptive sampling strategies) into the drug discovery pipeline to increase throughput and predictive accuracy
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-state model will be approximated using machine-learning surrogates and will be used for a real-time optimization, such that the plant operates optimally despite disturbances. The candidate will be part of
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to communicate the accomplished / in-process work to internal and external stakeholders and adapt directions where needed. Where to apply Website https://www.imec-int.com/en/work-at-imec/job-opportunities/machine
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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
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duties as assigned. REQUIREMENTS: REQUIRED: PhD in in computer vision, machine learning, artificial intelligence, or a closely related field. Strong programming skills. Strong background in machine