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Field
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The PhD position is part of the MSCA Doctoral Network PANDORA on discovering Pathomechanisms and Novel Drug Optimization for AMD (Age-Related Macular Degeneration). AMD is the most common cause of blindness
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suitable wireless link at any moment. In this PhD project, you will develop novel concepts for intelligent hybrid RF–OWC networks that optimize wireless service delivery in real time. Research topics include
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PhD Position Simulating the Airspace of the Future: Fleet & Network Optimization for Future Aircraft
current and future aircraft models + Evaluating how these differences affect fleet assignment and network planning + Optimizing airline operations (e.g., fleet composition, scheduling, routing) based on new
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PhD Position Simulating the Airspace of the Future: Fleet & Network Optimization for Future Aircraft
: Model development: + Characterizing the key performance differences between current and future aircraft models + Evaluating how these differences affect fleet assignment and network planning + Optimizing
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optimization of semantic communication in 6G multi-robot teleoperation scenarios. The candidate will investigate innovative approaches leveraging semantic communications, artificial intelligence, and emerging 6G
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to science, engineering and society. You will focus on optimization-based, data-driven and partially model-based control methods. You will have access to a strong research network and a broad range of
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) / PhD Position (m/f/x) (100 %, salary plus allowances package according to the Marie Skłodowska-Curie Actions (MSCA) – Doctoral Networks rules) for a period of 3 years. Selection is based on suitability
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to brain networks and psychosocial systems”, PN-IV-P6-6.1-CoEx-2024-0139. Where to apply E-mail [email protected] Requirements Research FieldAllEducation LevelMaster Degree or equivalent Skills
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available in Germany within the Marie Skłodowska-Curie Actions (MSCA) Doctoral Network TRILOGY, funded under Horizon Europe. The selected doctoral candidate will work on an interdisciplinary research project
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dissipative networks. We will view learning as a feedback interconnection of continuous-time (circuit) dynamics and an optimization algorithm. The key idea is to develop algorithms that minimise cost functions