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, GROMACS, or ASE), machine learning potentials (MACE), DFT calculations (VASP), programming (Python, PyTorch), and knowledge of nanofluidics, statistical thermodynamics, and data analysis. Website for
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machine-learning techniques. While recent theoretical work has examined market outcomes under algorithmic pricing, the determinants of firms' adoption and switching of such technologies remain poorly
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fingerprints, chemical similarity) • integrative multi-omic analyses through chemometrics, Machine Learning and AI He/She will be responsible for data processing and scientific dissemination of results
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will be responsible for designing and implementing physics-informed machine learning strategies for identifying constitutive laws in granular media. This includes the development of thermodynamically
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integrative approaches at the interface of (epi)genomics, machine learning and physics, addressing key challenges in modern quantitative biology. The successful candidate will be responsible for: • Develop
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statistics is required (Bayesian and frequentist approaches; knowledge of machine learning would be an asset). Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR9191-ARNLER-009/Default.aspx
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spectrometry, nanomaterials, bioinformatics, or machine learning would be an asset, though expertise in all these areas is not required. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant
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pair distribution function (PDF) analysis. Machine learning approaches will be used to support these analyses and provide a detailed description of crystal defects, atomic disorder, and their evolution
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, including model-based, observer-based, and machine-learning approaches - Identifying markers of intelligent, intentional, and malicious manipulations of detection systems - Developing detection and
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multimodal metabolomics using LC-HRMS/MS, GC-MS/MS, and advanced NMR approaches. - **Activity 4:** Multivariate statistics and machine learning to identify microbial and chemical biomarkers of resilience