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protocols, and test hypotheses and analyse scientific data. You will be expected to contribute ideas for new research projects, develop ideas for generating research income, present detailed research
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. The role includes implementing developed techniques and algorithms in high-quality software that adheres to group standards and evaluating them on large-scale benchmarks. You will take an active role in
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Systems and Software Program (WASP ). You can find more information about us on the Department of Information Technology website. The position is hosted by the Division of Scientific Computing (TDB), one
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conducts theoretical, methodological and applied research, with emphasis on statistical methodology for large-scale and register-based data and on applications of societal relevance. The successful candidate
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for this position have a great interest and knowledge in data analysis, filtering and modeling based on large amounts of data. It is important that you have a solid understanding of mining and mining systems to also
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ice, using a framework consisting of an ocean Large Eddy Simulation (LES) and a Discrete Element Model (DEM) of sea ice. Results from these simulations will be validated against a combination of in-situ
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; familiarity with time series analysis and complex survey design and weighting is preferred. Experience in epidemiological data analysis of large datasets using R and/or STATA is highly preferred, competency in
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for flexibility in the start and end dates, subject to approval by the department and the funding agencies. The successful candidate will join the Machine Learning & Data Science research group and conduct research
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Patient Advisory Groups (PAGs), conducting qualitative interviews, running focus groups, designing and executing large-scale international surveys with quantitative data analysis, and collaborating
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learning from limited data, cross-sensor integration, and scalable reasoning over large-scale time-series data, with applications in disaster response, urban analytics, and environmental monitoring. The team