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Field
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, statistical and machine learning, involving analysis of biological multi-modal and multivariate data, or related fields, is a requirement. Experience with computational modeling in metabolomics and metabolic
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested in working with machine learning for batteries, with
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. Documented knowledge and experience in computational metabolomics, computational biostatistics, statistical and machine learning, involving analysis of biological multi-modal and multivariate data, or related
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, providing excellent opportunities for the prospective candidates to expand the professional network and acquire useful multidisciplinary skills and qualities. Please see our latest publication in Science
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imaging technologies. Strong programming skills in at least one scientific programming language. Solid understanding of statistical methods, machine learning, and/or image analysis pipelines. Strong written
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scientific curiosity Mastery of data visualization and scientific communication Extensive knowledge of relevant machine learning and AI techniques Self-motivated individual with ability to work independently
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into mechanosensory corpuscles (Nikolaev et al., Sci Adv 2020; Nikolaev & Ziolkowski et al., Sci Adv 2023; Ziolkowski & Nikolaev et al., Sci Adv 2025). This is just the beginning and there is still much to be learned
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software related to the medical field Experience of specific software and programming languages, specifically ones suitable for machine learning, e.g. PyTorch or TensorFlow. Strong ability in spoken and
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and expertise in brain imaging (MRI), image processing and machine learning. Coordinating projects within the research group, supervising students and writing applications are also included in the role
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/department-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We focus on data-driven models for complex and temporal data