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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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disease. Key responsibilities Lead and conduct the processing and statistical analysis of large-scale long-read RNA and DNA sequencing, single nuclei RNA sequencing and spatial transcriptomics
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on complementary aspects of the project. Collaborate with the statistical post-doc on the study who will lead advanced statistical developments. Ensure methodological rigor and scientific excellence throughout all
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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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Computer Science, with specialization on applied machine learning, statistical methods, and/or autonomous systems Strong programming skills Strong analytical skills Industry experience in information and
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scientific publications on these techniques and their applications. WHAT WE ARE LOOKING FOR THE IDEAL CANDIDATE You have a PhD degree in Bioscience Engineering, chemical engineering, Statistical Data Analysis
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of i) advanced statistical models of life course trajectories of migrants and their descendants, and ii) the integration of such models into microsimulation models of population dynamics in the subgroups
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for identification of targets for early detection, prevention or treatment of Alzheimer’s disease. Position Lead and conduct the processing and statistical analysis of large-scale long-read RNA and DNA sequencing
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or treatment of Alzheimer’s disease. Position Lead and conduct the processing and statistical analysis of large-scale long-read RNA and DNA sequencing, single nuclei RNA sequencing and spatial transcriptomics