70 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "The Institute for Data" uni jobs at Chalmers University of Technology
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Helps us to derive novel climate data by combining two of Europe's new satellite sensors. If you have interests in physics, climate and machine learning, this is the Doctoral student position
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combining two of Europe's new satellite sensors. If you have interests in physics, climate and machine learning, this is the Doctoral student position for you! About us Our team is part of the Division
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at the Division of Data Science and AI at the Department of Computer Science and Engineering . Join our innovative team and contribute to exciting research in theory of machine learning, in a collaborative and
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. The main research problems include mathematical theory, algorithms, and machine learning (deep learning) for inverse problems in artificial intelligence, as well as application to medical problems. About the
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We are looking for up to three new PhD candidates who are interested in joining AI and Machine Learnings in the Natural Sciences (AIMLeNS) group. The group’s main research areas are AI and Machine
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an innovative spirit, in close collaboration with wider society. Chalmers was founded in 1829 and has the same motto today as it did then: Avancez – forward. Where to apply Website https://academicpositions.com
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60 credits* in Computer Science, Electrical engineering, or equivalent. You will need strong written and verbal communication skills in English. Strong machine learning fundamentals (probability
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methods relying on machine learning, artificial intelligence, or other computational techniques. The applicant is expected to develop and apply data-driven and machine learning-based methods. Special
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cases, this can be slightly prolonged). The position will be placed at Timur Shegai's research group at Chalmers. You can find more information about the group at: www.shegai-lab.com or https
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-order modeling, or machine learning Experience collaborating in interdisciplinary research teams What you will do Develop hybrid quantum–classical methods to improve simulation and prediction