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Field
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scientific data handling Familiarity with: Multi-agent systems or autonomous systems Optimization under constraints Experience in: Knowledge graphs, NLP, or information extraction (a strong plus) Application
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programming, microfluidics, and engineering to design sophisticated algorithms for exploring and manipulating information encoded in DNA-based graphs. The position is fully funded by a prestigious CNRS
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and PBWT-based algorithms (RaPID, RAFFI, FiMAP, ROH analysis, local ancestry inference), now extending into GBWT/RLBWT-based pangenome indexing, efficient pangenome graph construction and query, cross
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-learning architectures for sequential data (e.g., Transformers, graph neural networks, state-space models). Experience with OpenCV, GPU-accelerated inference, Docker, and modern software engineering
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. Solid background in signal processing, statistics, or computational neuroscience, preferred. Outstanding presentation skills, preferred. Experience in graphing, statistical analysis and data management
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, represented as a set of raw sequences, graphs, or sequence alignments. In this project, we will conduct a comparative study of noncoding genomic regions. You should have expertise in algorithm design and high
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to understand disease resistance and local adaptation across Europe. The project combines large-scale population genomics, graph-based pangenomics, structural variant discovery, genome-wide association studies
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, represented as a set of raw sequences, graphs, or sequence alignments. In this project, we will conduct a comparative study of noncoding genomic regions. You should have expertise in algorithm design and high
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, such as building web interface, web mapping, spatial database, and web services. Experience and expertise in graph-based deep learning for intelligent transportation infrastructure and systems
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novel hypotheses, ideas, and develop novel methodologies. Contribute and participate in the authorship of research publications and presentations. Collect, analyze and graph data, conclude research