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learning, epigenomic data, and mechanistic modelling. The mission is to contribute to the development of predictive models of the replication initiation probability landscape (IPLS) from limited experimental
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computational models and machine learning methods, as well as experience in repertoire data analysis. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR8023-CLAMAR-001/Default.aspx Work
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of complex mechanisms in large biological objects. (3) Computational modeling of transition pathways in high-dimensional systems. Prior experience in computer programming, machine learning algorithms, and
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, ideally molecular dynamics and/or DFT. Scientific programming skills, particularly in Python, are expected. Familiarity with machine learning or generative AI methods applied to materials would be a strong
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interaction of magnetic skyrmions in nanodevices. The second objective will be to develop an inverse-design code combining micromagnetic simulations and machine learning methods, in order to optimize skyrmionic
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of Research Experience1 - 4 Additional Information Eligibility criteria We are looking for a doctor in particle physics with less than two years of experience after the PhD. Experience in machine learning and
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comparison with predictions from machine learning models • Close collaboration with researchers in charge of machine learning, algorithmic architecture and performance analysis • Contribution to the scientific
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lie at the crossroads of multiple disciplines and involve expertise in optics, electronics, image and data processing (including machine learning), photophysics, chemistry and biology. The position is
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communications that are viable, efficient and compatible with physical and human reality. Our work is based on mathematical and computer theories for the development of models and algorithms, validated by hardware
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with physical and human reality. Our work is based on mathematical and computer theories for the development of models and algorithms, validated by hardware and software implementations. By relying