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of complex molecules and reactions. Must have PhD in Physical/Theoretical Chemistry or Artificial Intelligence/Machine Learning or related fields. This position will be initially awarded for one year, and
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twins, optimization, and control. In this PhD project, you will develop a new systems and control theory for learned operators, bridging modern scientific machine learning with classical control theory
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player who contributes positively to collaboration and project success. You also possess: a PhD in Artificial Intelligence, Machine Learning, Computer Science or a related field; at least 3 years of hands
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analysis, and data visualization. Apply advanced statistical, machine learning and AI methods when appropriate. Review analysis outputs and ensure methodological consistency and quality. Collaborate with
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Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. | Dortmund, Nordrhein Westfalen | Germany | about 10 hours ago
. or Diploma in bioinformatics or a comparable qualification Extensive programming experience Practical experience in machine learning and the application of large language models Knowledge of OMICS and image
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and sustainability; Investigate and apply artificial intelligence and machine learning techniques, including large language models (LLMs), across CENSE’s scientific body in its five thematic areas
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is seeking a highly motivated PhD candidate to work on a fundamental research project on systems and control theory for learning in neuromorphic circuits. Neuromorphic computing is an analog, brain
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in epidemiology, causal inference, genetic epidemiology, and machine learning. As a PhD candidate in the project, you will: Actively participate in group meetings, design statistical analysis plans in
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within the Division of Artificial Medical Intelligence of Department of Ophthalmology in the University of Colorado School of Medicine. We focus broadly on quantitative and machine learning techniques in
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Join us in developing machine-learning accelerated simulation methods to understand and optimize interfaces in hybrid organic-inorganic materials for sustainable energy devices. Your work