191 programming-"https:" "https:" "https:" "https:" "https:" "Data driven Materials Modeling" research jobs in Sweden
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. Experience and expertise in human mobility simulation and prediction with agent-based modeling and deep learning techniques. Proficient in Python programming for geospatial data processing, modeling, and
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. Documented practical experience in high-plex image analysis of tissue Documented knowledge and practical experience in programming and data analysis in R. Good communication skills and extensive documented
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Postdoctoral fellow within Microglia–Neural Circuit Interactions in Memory and Disease (PA2026/2262)
research projects within the lab’s research program Perform in vivo calcium imaging and behavioral experiments Analyze neuronal and microglial activity datasets (e.g., calcium imaging) Conduct
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analyzing patient-derived samples. Methods used include analysis of genetic data, brain imaging data, health-registry data, and protein biochemistry, using statistical programs such as R, SPSS, PLINK
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, Infrastructure and Cybersecurity, and Graphic Design and Web Development. We also offer a popular master’s programme in Cybersecurity. For more information, visit Department of Computer Science and Informatics
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. Curriculum vitae, including a list of publications. A writing sample (e.g., article, dissertation chapter). A research plan for your project (5 pages) Contact details of two academic referees.
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: Quantitative remote sensing, including optical and radar data. Plant science/ecology, especially related to forest ecosystems. Computer programming. Data analysis (machine learning, statistics, numerical
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implementation research program aimed at bridging evidence from the Umeå HIT studies into scalable real-world practice in both digital and community settings. Previous work within the Umeå HIT project has
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in teaching, mainly in our international MSc-programme in Logistics and SCM, by supervising master student thesis writing and taking part in PhD-student supervision. Qualification requirements
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predictive deep learning models, and physical mechanistic models (thermodynamic and kinetic models etc.). Examples of suitable backgrounds: machine learning, programming, mathematics, physics. You will