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expertise in machine learning or computational modelling who are eager to advance conceptual innovation toward practical industrial deployment. Qualifications PhD in Computer Science, Machine Learning
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senior research position to work on projects related to computational analysis of mass spectrometric datasets. A major focus will be on the application of AI/machine learning models and other computational
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of the following subjects: scalable data management, systems for machine learning, distributed and parallel systems, or cloud-based systems. We are especially interested in researchers who build working systems and
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professional disciplines, the university is well known for its learn- by-doing approach and Teacher Scholar Model. The university is noted for its scenic and historic 1,400-acre campus, which was once the winter
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research. You will strengthen the data science and machine learning activities of IAS-9 by developing core AI methods with applications to electron microscopy and materials discovery. You will work in a team
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questions and data of CREATE. The successful candidate will conduct advanced methodological and psychometric research. Potential topics include (a) AI, machine learning, and large language models
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, or R&D environment. Experience developing CAD models and generating CAM toolpaths for CNC machining of metals and plastics. Experience performing assembly and testing of experimental equipment
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marketing strategy, supply chain and logistics, digital distribution, and operations research. Leveraging the latest technologies, such as artificial intelligence, machine learning, and large language models
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student in areas related to Computer Science. Basic experience in health data analysis. Python programming. Knowledge of machine models and deep learning. Intermediate English level. Specific Requirements
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) are required - Experience in working with Earth system model simulations is required - Experience in machine learning is required - Willingness to travel for work (project meetings, workshops, and research