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/Machine Learning (AI-ML) approaches to meeting this challenge. Possible topics include, but are not limited to: storylines for plausible narratives of regional climate change, novel algorithms for rare
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challenge. This project aims to explore data-driven Artificial Intelligence/Machine Learning (AI-ML) approaches to meeting this challenge. Possible topics include, but are not limited to: storylines
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working with digital signal processing, advanced filtering techniques, dynamic feature extraction, time-domain and frequency-domain analysis, signal fusion and machine learning to enhance Crainio system
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fluids, flow-induced pattern formation in both simple and complex flows (e.g. flow instabilities, product defects), multiscale analysis, and the application of machine learning techniques. About the
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area, with content covering robotics and machine learning, and excellent programming skills in Python. You should have research experience in either robotics or machine learning. You should also have
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students and PhD students. Applicants will have, or be close to completing a PhD in a relevant field and possess relevant experience, in the area of probability or statistical machine learning. They will
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following position Postdoctoral researcher (m/f/d) in Environmental Data Science and Machine Learning for the project BoTiKI Location: Görlitz Employment scope: full-time (40 weekly working hours) / part
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, or other Maxwell solvers. Experience with machine learning algorithms is an advantage but not required. General qualifications Scientific production and research potential at the international level
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simulations using, e.g., COMSOL, Lumerical, or other Maxwell solvers. Experience with machine learning algorithms is an advantage but not required. General qualifications Scientific production and research
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). The emergence of data-driven techniques (broadly grouped under the term “machine learning”) challenges the traditional foundations of controls and represents an alternative paradigm that cannot be ignored