191 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" positions in Sweden
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and machine learning, we collaborate globally to monitor environmental change and support a sustainable future. About the research project The postdoc will work at Chalmers University of Technology in a
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and mixed-signal design, and thus broaden and strengthen our expertise in the design of electronic systems. You will also develop and teach courses in electronics design at the bachelor and master
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researchers develop new machine learning (ML) methods to tackle challenging molecular engineering problems in life sciences and materials design. Situated in the Data Science and AI division , our group
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Experience in machine learning Knowledge of SDN and NFV Knowledge of basic TCP/IP protocols What you will do Conduct high-impact research and publish in leading journals and conferences Shape research
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Extensive knowledge of relevant machine learning and AI techniques Self-motivated individual with ability to work independently Teaching and mentorship abilities or interests in personal development A
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/NIR) for separation and material sorting, and use machine learning for process optimisation and performance prediction from fiber to finished product. Functional processing of recycled materials and AI
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independently and good interpersonal skills previous experience of working with industrial partners experience of scientific publishing in machine learning and artificial intelligence. Merits: proficiency in
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, electromagnetics, optimization, machine learning, and networking. Strong documented experience in these areas is commendable, particularly by having published your work. Candidates should have an excellent mastering
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We are seeking a highly motivated doctoral student to develop ship physics-integrated machine learning models for real-time prediction and optimization of wind-assisted ship propulsion systems
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: S. Aalto). In the project we use multi-wavelength techniques, including recently developed mm and submm observational methods, to reach into the dark hearts of dusty galaxies. New machine learning