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Field
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of the ERC Consolidator project AUTOMATIX (see details below), we are seeking a PhD candidate to develop machine learning approaches for constitutive modeling. Context With the advent of machine-learning (ML
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of results at conferences interaction with team members and international collaborators Required skills : Degree : PhD in computer science, machine learning, or computational biology We expect a candidate with
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Information Eligibility criteria Scientific and technical skills: • PhD in applied physics, optics, astrophysics, control systems, or artificial intelligence. • Strong background in machine learning
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Differential Imaging (CDI) to exploit the fundamental property of light coherence. The PhD will focus on two complementary approaches: 1) Enhancing CDI with machine learning: improve this technique using
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represents a major bottleneck for the study of complex catalytic interfaces. Objectives The objective of this PhD project is to develop data-efficient machine learning strategies to study CO₂ hydrogenation
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of: • machine learning • cybersecurity • distributed systems • privacy-enhancing technologies The research will be carried out within the (team name) at LS2N, focusing on trustworthy AI and cybersecurity
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, energy consumption, and packet loss. The use of distributed machine learning provides a relevant solution to mitigate the lack of communication reliability [3][4]. This PhD proposes to guide the learning
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significant computational component. We strongly recommend a background in machine learning and coding. Applicants with a background in areas such as computational neuroscience, reinforcement learning, or deep
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LanguagesFRENCHLevelBasic Research FieldMathematicsYears of Research ExperienceNone Additional Information Eligibility criteria Degree : PhD in computer science, machine learning, or computational biology We expect a
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, computational neuroscience, bioinformatics, robotics, or a related field Strong expertise in computational data analysis (e.g., behavioral analysis, signal processing, or machine learning) Experience working with