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project, a machine learning algorithm will be developed for magnetic systems. Specifically, the project aims to solve non-linear magnetic equations (e.g. macroscopic Maxwell equations) using physically
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of teaching Your Qualifications: The successful candidate must hold a Doctorate/PhD degree or equivalent in machine learning or closely related field, such as Computer Science with strong focus on machine
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fields. Proven knowledge in artificial intelligence and machine learning applications (e.g., PyTorch, TensorFlow). Familiarity with design computation tools like Grasshopper for Rhino3D and experience in
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for the position of one to two Prae-Docs (3 year appointment, 30 hours) at the intersection of Mathematical Finance, Stochastic Analysis and Machine Learning. The successful candidate will work in the group
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Machine Learning. The successful candidate will work in the group of the START research project "Universal structures in Mathematical Finance", led by Prof. Christa Cuchiero. The focus of the project lies
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of one to two Prae-Docs (3 year appointment, 30 hours) at the intersection of Mathematical Finance, Stochastic Analysis and Machine Learning. The successful candidate will work in the group of the START
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diagnostic imaging methods, as well as associated machine learning applications to strengthen, expand and cross-link existing research topics within the university. Additional Information Selection process The
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able to teach in German within three years from the signing of the contract Advanced computer skills are desirable. Ability to work in a team. Organisational skills. Additional Information Benefits What
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disciplines relevant for the position. Outstanding scientific qualification and relevant doctorate in the field of high-voltage engineering Experience in the application of machine learning in power engineering
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intelligence (AI)/machine learning (ML) methods to early detect and predict cardiovascular diseases from real-world, multimodal clinical data Identifying novel, e.g., AI/ML based, digital biomarkers