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materials and technologies. Using advanced computational modeling and machine learning, we seek to elucidate the mechanisms governing the self-assembly of lignin in different solvents and the formation
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learning models to link sequence to function, supporting the design of regenerative therapies in the nervous system. The position will combine hands-on neuroscience experimental approaches with data-driven
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partnership between academia and industry to drive research and development forward. Project description This project aims to develop unsupervised machine learning methods for extracting dynamical models
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models”. The scholarship is full-time for two years with access in spring 2026 or as agreed. Departmental specific information We work on sensory biology and mechanotransduction, focusing on non-standard
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at the Department of Medical Biochemistry and Biophysics, which offers an international, collaborative, and open-minded research environment. Please visit the lab’s webpage for more information: https://www.umu.se/en
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statistics, unsupervised machine learning, optimisation, model predictive control. Experience in financial mathematics. Having high integrity, be process-oriented and able to work independently. Being able
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, regression models or machine learning. Applicants from clinical hepatology with experience in the above fields can also be interesting. You will work in an interdisciplinary and international research
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manufacturing. It is meritorious to have previous experience in data analysis and processing with Python (or similar), preferably including documented experience with machine learning tools. It is meritorious
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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
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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