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. The position bridges machine learning and molecular science, with opportunities for collaboration, mentorship, and impactful research. About us The Department of Computer Science and Engineering (CSE
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at: https://www.umu.se/en/department-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models
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risk factors. The main objective is to design and apply machine learning and deep learning methods to understand and investigate the functional behavior of gender-specific cancers. The work will include
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, development of chemical process solutions for repurposing of electrodes, and integration of AI-based vision and active machine learning to optimize the efficiency of the process. Writing publications and
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the last three years prior to the application deadline. Experience in some of the following areas is meritorious: AI and machine learning; convex analysis; functional analysis; mathematical statistics
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–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience collaborating in interdisciplinary research teams A doctoral
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science, machine learning, automated systems, or a closely related field Have experience working with ruminants Have experience in precision agriculture and/or precision livestock farming Have experience
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engineering, precision agriculture, data science, machine learning, automated systems, or a closely related field Have experience working with ruminants Have experience in precision agriculture and/or precision
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of Technology and the University of Gothenburg, combining the best of two academic worlds in the heart of Gothenburg. At the Division of Applied Mathematics and Statistics , we conduct research in computational
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techniques and generative AI. A strong background in software engineering as well as some interest in machine learning will be required for this project. Interest in compilers and the Java Virtual Machine