877 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "U.S" positions in Sweden
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software integration for all equipment within the Lab. Supporting the research community in building and integrating their equipment to the facility, with focus on keeping people and machines safe. Would you
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. Our research is focused on cell biology, spatial proteiomics and machine learning for bioimage analysis. The aim is to understand how human proteins are distributed in time and space, how this affects
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information about us, please visit: www.dbb.su.se . Project description The candidate will develop machine learning (ML) strategies, primarily revolving around interpretable ML and generative AI, to study
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are united in our efforts to understand, explain and improve our world and the human condition. Work duties The work involves field studies and associated computer, greenhouse and laboratory work as part of
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student in Statistics who can perform high quality statistical research. Apply January 6, 2026, at the latest. We are seeking a PhD student within the WASP-HS project “Machine learning to study causality
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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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, military service or similar, or service/assignment of relevance to the appointment. Good knowledge of English, both spoken and written, is a requirement as it is the working language of the project
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department is available at: Department-of-computing-science Project description Our societies rely on computer systems and on software stacks. Unfortunately, software systems contain bugs and vulnerabilities
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Information Technology works broadly with research within Cryptography, Computer Security, Wireless, and Fixed Networks. The security group has around 20 members. The main research directions are Cryptography
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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