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collaboration with our wet-lab team and collaborators, you identify and integrate validation data In collaboration with the VIB.AI ML unit, you manage a large codebase containing the harness, interpretation
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observational sources include data from field campaigns, long-term observational deployments, and operational satellite and radar. The numerical experiments will include large-eddy simulations over large domains
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. - Programming and Workflow Automation: Proficiency in writing Python and Shell scripts to automate modeling and analysis processes. Ability to efficiently manage large amounts of data and configure robust
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, computational biology, genomics, or a related quantitative field. You will have strong programming skills (Python, R, Linux) and experience analysing large-scale sequencing datasets, ideally RNA-sequencing data
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, machine learning, generative AI and related fields as part of the DataSig II grant “High order mathematical and computational infrastructure for streamed data that enhance contemporary generative and large
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datasets High proficiency in data analysis and statistical programming using R and/or Python. Experience working in high-performance computing (HPC) environments for large-scale analyses and with workflow
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learning, decision-making, belief updating) Coordinating large database of computational task data for harmonized analyses Modeling individual differences and within-person change over time in a longitudinal
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of research investigating cancer risk and prevention. The appointee will work closely with Professor Ruth Travis, Dr Karl Smith-Byrne and other members of the research team, using large-scale epidemiological
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framework for the joint analysis of large-scale structure (LSS) and gamma-ray data. By combining these complementary probes of the same underlying matter distribution, the project aims to sharpen constraints
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Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to biological data collection, extraction, and standardization