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strong interest in computer science (software development, machine learning techniques, etc.) is desirable. · Applicants must have a maximum of 3 years of research experience after the PhD. · Language
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. T. (2022). Quantitative brain morphometry of portable low-field-strength MRI using super-resolution machine learning. Radiology, 306(3), e220522. [Winter2024] Winter, L., Periquito, J., Kolbitsch, C
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interdisciplinarity, blending machine learning, computational creativity, and musicology. It bridges AI methods—like generative models—with musical structure, theory, and cultural contexts, emphasizing data-efficient
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[MSP+21, KKK16]) but is lacking for distributed systems. Currently, software systems for distributed systems are typically structured in terms of separate programs that are deployed on different machines
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interest for the machine learning and neuroscience communities How to apply... Applications should include: Curriculum Vitae Cover letter Early application is highly encouraged, as the applications will be
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, machine learning, remote sensing, and oceanography to tackle the challenges of capturing and interpreting complex geophysical processes. 1.5. References [1] Torres, R., Snoeij, P., Geudtner, D., Bibby, D
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 3 months ago
for the management of electricity markets. At the scientific level, the integration of pollution constraints and learning models into ``multi-leader single follower" optimization models represents a major challenge
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Inria, the French national research institute for the digital sciences | Lille, Nord Pas de Calais | France | 3 months ago
industrials (e.g. Google, Microsoft, IBM, etc.). This being said, the noisy and the limited capacities of today’s gate-based quantum machines make it very tricky, sometimes impossible, to apply most of them
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combined with Machine Learning algorithms, this thesis aims to study long-term temporal trends in tropospheric ozone (O3) in Europe across different type of environments (background, rural, urban), while
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highly interdisciplinary, integrating network engineering, IoT device management, machine learning, and cybersecurity. It blends protocol optimization (Coreconf/YANG) with LLM-driven automation, enhancing