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department's activities is available at https://www.uu.se/en/department/medical-sciences Project Description This doctoral position is part of HEPARD (Health Economic Policy Advice with Real-World Data), a Marie
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expertise in research and development in the following areas of AI and Data Science : Machine and deep learning, NLP, BDI (Belief-desire-intention) systems, and Large Language Models (LLMs). Expertise in
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of Mathematics and Natural Sciences at Heinrich Heine University Düsseldorf is inviting applications for the position of a Professorship for Machine Learning (open rank: W2 or W1 with tenure track to W2
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The Centre for Machine Learning within the Data Science and Statistics Section of the Department of Mathematics and Computer Science (IMADA) at the University of Southern Denmark invites
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well as familiarity with machine learning workflows, natural language processing (NLP), and text-as-data methods. We are especially interested in applicants who demonstrate a strong substantive interest in using
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programming targeting analyses of large-scale assessment and/or survey data ●Advanced proficiency in Python for data analysis, machine learning, and NLP tools ●Experience with relevant statistical programming
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that support the unit for area protection and marine spatial planning, as well as operations at SLU Aqua. Your profile You have documented expertise in marine ecology and computer vision and machine learning
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Position as Computational Analyst / Bioinformatician in RNA Therapeutics and Cardiometabolic Disease
seq like PSCSR-seq. Training PhD students and post-docs to analyze large-scale datasets and interpreting results. Provide input to projects in our group and help with illustrating complex biological
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on real-world health data analysis — including study design, data wrangling, phenotype development, data integration, and statistical and machine-learning methods — to accelerate project delivery
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at Sahlgrenska Academy of relevance include genomics, metagenomics, culturomics, proteomics, transcriptomics, software development, machine learning, and other statistical analyses of large-scale health data