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epidemiology. Experience analyzing large cohort, registry, biobank, or omics datasets. Strong programming skills in R, SAS, Stata, or Python. Familiarity with survival analysis, longitudinal data analysis
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community. The research will be carried out at the Department of Information and Communications Engineering, DICE, at Aalto University, Finland. The project environment offers excellent infrastructure
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established and emerging bioinformatics, statistical modelling, and machine learning approaches to analyse large-scale datasets, including bulk and single-cell sequencing, gene expression arrays, proteomics
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an unusual combination of challenges: data are sparse and unevenly distributed, observations are uncertain, and decisions about where to survey unfold sequentially under significant time and cost constraints
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30 Jul 2026 Job Information Organisation/Company CNRS Department Institut Pierre-Simon Laplace Research Field Environmental science Environmental science » Earth science Environmental science
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of both hardware and software tools for operando characterization under electrochemical conditions, as well as the management and analysis of large experimental datasets. The successful candidate will
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, interdisciplinary environment. Additional Qualifications Prior experience with one or more of: ● Health claims data, EHRs, or other large-scale health/administrative datasets. ● Environmental, climate, or air
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of large-scale geospatial and time-series datasets. The candidate will develop and evaluate multimodal AI models to characterize vegetation and land-surface dynamics and quantify ecosystem responses and
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computational background in statistical modeling, reproducible workflows, and large-scale data processing Experience with water quality, nutrient, sediment, watershed, or hydrologic datasets Experience developing
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Research support for conference travel and data collection A vibrant campus with an innovation park and a large spin-off portfolio, in a management research cluster that includes HEC Lausanne and IMD within