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/or MATLAB). Experience with C++ and maintenance of complex software implementations. Experience with CUDA is a strong plus. Ability to design, conduct, and analyse controlled experiments, both in
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open to candidates with a strong interest in either: i) Radio/physical-layer intelligence (e.g., channel estimation, CSI prediction, edge-deployable deep learning), or ii) Networking and control-plane
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qualities are in line with the faculty and university research policies . You act with attention to quality, integrity, creativity and cooperation. You have very good command of English and French. Knowledge
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) Radio/physical-layer intelligence (e.g., channel estimation, CSI prediction, edge-deployable deep learning), or ii) Networking and control-plane intelligence (e.g., reinforcement learning for scheduling
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AI-driven control loops can improve network robustness, service continuity, and fault tolerance across heterogeneous and multi-domain environments. The PhD candidate will explore innovative mechanisms
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aims to investigate how network intelligence and automation, distributed core network designs, and AI-driven control loops can improve network robustness, service continuity, and fault tolerance across
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related discipline (or an equivalent degree). Prior research experience in qualitative methods (e.g., ethnography, interviews, focus groups) and/or historical/archival research. Excellent command
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sequencing data or proteomics data Experience in wet lab molecular biological techniques. Programming experience demonstrated with good programming practice (maintaining code repositories, version control, R
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techniques. Programming experience demonstrated with good programming practice (maintaining code repositories, version control, R, Python). Experience in working with complex datasets. Interest in
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under controlled conditions. Both the mock-ups and selected historical objects will be analysed at defined intervals using advanced imaging and spectroscopic techniques (FTIR, XRD, macro-XRF