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of statistical shape modelling. Position The project aims to advance personalized musculoskeletal care by transforming routinely available imaging data, such as 2D X-rays and increasingly common surface scans
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Mathematics » Statistics Researcher Profile Recognised Researcher (R2) Application Deadline 9 Feb 2026 - 22:59 (UTC) Country Belgium Type of Contract Temporary Job Status Full-time Is the job funded through
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related to staff position within a Research Infrastructure? No Offer Description Post doc: Leading the methodological and statistical development at the core of a large‑scale, innovative project to create
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statistical and geospatial analyses (e.g., using ArcGIS, SPSS, Stata, Python, R). Conduct cross-city comparative research Map and analyse the geographical distribution of eviction patterns within and across
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under study, i.e. Brussels, Amsterdam, Barcelona, and Thessaloniki; Develop comparative indicators of urban eviction rates; Conduct advanced statistical and geospatial analyses (e.g., using ArcGIS, SPSS
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and/or empirical work on individual choice data is an asset, and a good understanding of the decision-making literature (both historical and recent) is mandatory. Familiarity with statistical coding in
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(an asset) • High proficiency in quantitative methods and statistical software (e.g., R) • Experience in open science practices. • Excellent communication skills in English (French is a plus) Note: Applicants
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biology, bioinformatics, genomics, statistics, physics, or a related quantitative field Demonstrated ability to analyse large-scale sequencing data using R, Python or equivalent A self-motivated team player
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Infrared Spectroscopy) and rheological assessment (i.e., Dynamic Shear Rheometer) of bituminous binders is a nice-to-have. You are keen on developing new knowledge in multivariate statistics and modelling
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venuesStrong programming skillsSolid mathematical foundation, including linear algebra, probability, statistics, and optimizationBroad and in-depth experience with machine learning algorithms and deep learning