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artificial intelligence methods. PhD position in atmospheric corrosion studies via novel experiments and machine learning Reference code: 50134137_2 – 2025/MO 1 Commencement date: as soon as possible Work
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/or spatial multiomics, advanced imaging, iPS cells, machine learning, and computational biology. The ideal candidate will have a passion for addressing fundamental questions in biology and an eagerness
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willingness to learn and apply machine learning approaches We offer A versatile and challenging job in a vibrant and world-class research environment operating at an international levelParticipation in a large
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, data scientific or machine learning background that is keen to work in an interdisciplinary environment and open to collaborating with researchers from other disciplines. The successful candidate will
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that are commonly applied to learning and teaching practice in computer science education. Employment Terms The PhD student is expected to teach FNUG’s courses MM107 Dynamic Systems and Interdisciplinary Subject
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of the project is to use machine-learning assisted molecular dynamics simulations incorporating quantum effects for the identification of new variant-specific drug targets which will be validated experimentally
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of predictive models for energy demand and production. These models will leverage techniques such as time series analysis and machine learning and will be integrated into a digital twin platform. The aim is to
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. Measurement techniques in field applications and in the laboratory. Modeling and simulation skills (batteries, energy systems, electric equivalent circuits). Machine learning, statistical analysis, and other
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materials science, physics, chemistry, electrical engineering (or a similar discipline) with focus on sensorics; experience in data processing and machine learning; experience in 2D materials synthesis and
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dynamics, data science, and machine learning are beneficial. What we offer: We offer a position with a competitive salary in one of Germany’s most attractive research environments. TUD is one of eleven