113 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"U.S" positions in Sweden
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of complex brain processes. The prospective PhD candidate collects brain MSI data and develops novel machine learning methods in connection to generative models such as flow matching. Therefore, the doctoral
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on innovative development and application of novel data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. The specific focus is on development and
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focus on innovative development and application of novel data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. The applicant is expected to develop
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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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loop/TAD structures. - Perform comparative analyses versus Populus tremula; apply network modelling and machine learning for regulatory inference. - Functional validation of candidate TE‑CREs in spruce
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research grants from funders relevant to a Swedish context. Ability to teach courses on adjacent programmes in the department, e.g., software development, human-computer interaction, embedded systems, etc
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aspects of software development (DevOps, Algorithms etc.) or informatics (e.g., content design, user experience design and human-computer interaction). You are expected to build and maintain an academic and
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(FMAN20) for MSc in Machine Learning, Systems and Control, during the period of 2026-08-31 – 2026-10-20. Computer Graphics (EDAF80) for MSc in Virtual Reality and Augmented Reality, during the period of
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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