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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
Statistical learning theory and complexity analysis Automated theorem proving and formal methods Random matrix theory and its applications in modern AI systems Requirements: PhD in Mathematics, Computer Science
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. Expected start date and duration of employment This is a 1-year Post Doctoral position (37 hours/week) from 01.02.2027. Job description You will be contributing to the development of catalytic methods based
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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and algorithmic perspectives on large language models Statistical learning theory and complexity analysis Automated theorem proving and formal methods Random matrix theory and its applications in modern
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, technicians, and administrative personnel. Please note that this position falls within a sector subject to protection of scientific and technical potential. Where to apply Website https://emploi.cnrs.fr/Offres
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methods that connect sensing, artificial intelligence, and decision-making for agricultural, food, biological, energy, environmental, and materials systems. The lab's core methodological focus includes
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Departmental Website: https://profiles.stanford.edu/louise-sun(link is external) How to Submit Application Materials: Please submit your application to Ariadna Renteria at [email protected] . Does
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sources Design reproducible studies and evaluate models using appropriate analytical methods Build and evaluate reproducible longitudinal models and research software Write scientific articles and
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employment in the project or within 12 years before January 1st of the year of employment in the project). Knowledge of numerical methods and high-energy astrophysics will be preferred
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of statistical and quantitative methods for analyzing complex, highdimensional datasets Good understanding of biochemical principles, including mass spectrometry Good programming skills (Python and/or R) Strong