63 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" positions at University of Lund in Sweden
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background and interest in soil microbial ecology, ecosystem ecology and biogeochemistry. You will be part of the Microbial Biogeochemistry in Lund (MBLU) research environment (https://portal.research.lu.se/en
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. Demonstrated experience in computational methods, particularly in deep learning and computer vision. Understanding or willingness to learn advanced statistical modeling is a plus Assessment criteria and other
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Description of the workplace The Division of Secure and Networked Systems at the Department of Electrical and Information Technology conducts broad research in cryptography, computer security
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the future of research? Find more reasons why Lund University and the HT Faculties is right for you here, and learn more about Working in Lund , Moving to Lund and Living in Lund . Qualifications
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thus MLOps (Machine Learning Operations), datacentric AI, and legal and ethical aspects of AI. The empirical research catalyzes industry-academia collaboration and cross-dsicplinary initiatives, in which
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, where the mentioned division formally belongs to LTH. Research at CMS currently covers areas such as pure mathematics, applied mathematics, mathematical statistics, as well as computer vision and machine
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equality and diversity as a strength and an asset. Work duties The work involves field studies and associated computer, greenhouse and laboratory work as part of the POLLCLIM project that focuses
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Security, Wireless, and Fixed Networks. The security group has around 20 members. The main research directions are Cryptography, Privacy and System Security for Connected Systems. We are now looking for a
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for machine learning, e.g. PyTorch or TensorFlow. Strong ability in spoken and written Swedish. Assessment of the applicants will primarily be based on scientific merits and potential as researchers. Special
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factor that strongly modifies turbulence, pressure drop, and heat transfer. Unlike conventional machined roughness, AM roughness is characterized by randomness, porosity, and powder adhesion, producing