111 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "Imperial College London" positions at Chalmers University of Technology
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contribute to exciting research in theory of machine learning, in a collaborative and dynamic environment. In the rapidly growing area of artificial intelligence (AI), this position is a uniqure chance to
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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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researchers at the Division of Materials and Manufacture and the Department of Physics at Chalmers as well as colleagues at KTH, the Royal Institute of Technology , Stockholm, Sweden, Imperial College London
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stochastic dynamics for shape change. A further aspect of the project is learning and calibrating these models from data using data-driven inference methods. Who we are looking for Required qualifications A
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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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, Computer Science, Bioinformatics, Machine Learning or related fields. You need excellent programming skills and an ability to design and implement machine learning-based projects. You need a strong
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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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of biomathematics, biostatistics, spatial modeling, differential equations, Bayesian inference, large-scale computational methods, bioinformatics, data science, machine learning, optimisation, numerical methods