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with computational analysis (Python or R and HPC environments), and a demonstrated, strong interest in genomics and/or polyploidy. Experience in DNA repair assays, cytogenetics, plant functional genetics
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 20 days ago
Engineering, Computer Science, Applied Mathematics or a related field. - A strong background in image processing or/and in computer vision is required. - Strong programming skills in Python. - Strong
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candidate will: Hold an MSc (or equivalent) in Physics, Mathematics, Computer Science, or related fields Have strong computational and modelling skills, including proficiency in Python and experience with
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, genetics, plant biology, or a related field, and have documented familiarity with computational analysis (Python or R and HPC environments), and a demonstrated, strong interest in genomics and/or polyploidy
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applications. Experience in natural language processing, model evaluation, or experimental design is advantageous. Proficiency in Python and/or R and familiarity with AI/ML libraries or generative AI platforms
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for data acquisition and analysis (e.g., Python or LabVIEW) are highly desirable. After a training period, you will operate and maintain the fast EC-STM system and contribute your own ideas to the project
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relevant field (Informatics, Computer Science, Ecology, or Biology). strong Python programming skills proven experience with Machine Learning and image processing (Deep Learning/Object Detection) strong
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, and cybersecurity. Programming skills (C, C++, Python, or Matlab). Eager to work within a team and independently. Ability to collaborate with industry and academic researchers. Fluent in spoken and
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Python and/or R for bioinformatics and ability to write clean, reproducible, and well-documented code for complex multi-step pipelines. have documented experience from cancer research. Additional
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analysis, and biology, as well as Python and R programming language (critical). Previous experience analyzing high-resolution spatial VDJ/transcriptomics, long-read sequencing, single-cell transcriptomics