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technologies for quantum photonic networks? We are now strengthening our research team on quantum light sources in silicon and are opening three PhD positions within fabrication, characterization, and theory
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: algorithmics, graph transformation and algorithm engineering. Exposure to systems chemistry or systems biology is an asset but not a must. Proven competences in programming and ease with formal thinking are a
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: algorithmics, graph transformation and algorithm engineering. Exposure to systems chemistry or systems biology is an asset but not a must. Proven competences in programming and ease with formal thinking are a
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Department of Chemistry invites applicants for two PhD fellowships in theoretical catalysis. The projects are rooted in the Theory group which is part of Center for High Entropy Alloy Catalysis
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directions will be pursued to enhance column generation using machine learning. The first line of research focuses on improving scalability by using Graph Neural Networks to identify and eliminate non
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. CLASSIQUE is organized into four research thrusts that rely upon interdisciplinary competences in: communication theory, networking, information theory, physics, mathematics, computer science, and statistics
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algorithms. Graph Neural Networks. The candidate is expected to hold a relevant MSc degree in Computer Science, Data Science, Physics, (Applied) Mathematics, Computational Statistics or another field
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demonstrated experimental realizations and proven theoretical advantages. The project may involve several aspects, including mathematical theory, algorithm development, error correction, adaptation of GBS-based
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for the study of quantum quenches. Who are we looking for? We are looking for a candidate who must hold a master degree in theoretical physics and have a strong background in quantum field theory and holography