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systems. Key Responsibilities Develop graph-based (multi-)omics analysis algorithms Benchmark graph-theoretic against graph-ML approaches Analysis of food-related (multi-)omics data Your Profile The ideal
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background in quantitative data analysis, mathematical modelling, and experience in methodologic development and implementation; (d) be able to demonstrate a high level of creative ability in research in
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or manuscripts in preparation. Experience with fMRI data acquisition and/or analysis (e.g., FSL, SPM, AFNI, fMRIPrep, nilearn). Proficiency in at least one programming language (e.g., Python, MATLAB, or R
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. Required PhD student academic qualifications: Honours/Msc degree in microbiology/bioinformatics, with specific emphasis on functional microbial multi-omic data analysis. Knowledge of a programming language
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BeFC and Univ. Grenoble Alpes, CNRS, 38000 Grenoble, France | Grenoble, Rhone Alpes | France | about 23 hours ago
16 Apr 2026 Job Information Organisation/Company BeFC and Univ. Grenoble Alpes, CNRS, 38000 Grenoble, France Research Field Chemistry Chemistry Technology » Energy technology Researcher Profile
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, including: Advanced training in mixed methods research (qualitative, comparative, and structured data analysis) Experience with international fieldwork in the Netherlands and the UK A publication-oriented PhD
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analysis Participation in administrative tasks of the institute Your Profile In-depth knowledge of transportation planning, road design and traffic data analysis Very good computer skills German and English
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Modelling, Applied Statistics, Linguistics, Data Analysis, Large Language Models, Machine Learning. Start date: 1st October 2026 Deadline: 30th April Duration: 36 months Funding: Funded Funding towards
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the service life of infrastructure. The research content includes: real-time data analysis and modeling pipeline network dynamic prediction and state identification optimisation algorithm and high-performance
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Health emergencies, mass casualty events and disasters generate large volumes of operational, clinical and organisational data. However, these data are rarely synthesised into structured, analysable