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Qualifications PhD degree in computer science, data science, or related fields. Effective written and verbal communication skills Robust machine learning and natural language processing skills Proficient computer
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, unlocking reliable perception and navigation where GNSS/GPS cannot be trusted or is unavailable. The project combines ultrasonic sensing, probabilistic perception, and machine learning with advanced robotics
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at the intersection of AI, RF, and wireless communication. Your main tasks include developing machine-learning methods for wireless interference detection, mitigation, edge intelligence, and applying AI to optimize RF
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methods to rigorously assess the safety and effectiveness of medications in real-world patient populations. Defining individualized treatment strategies: Leveraging traditional and causal machine learning
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biomedical image processing. Within the scope of machine learning and computer vision, there will be freedom to suggest your own research directions, and to become acquainted with new techniques and approaches
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topics: Free space optical communication Visible light communication DSP for coherent optical communication Machine learning and AI-native physical layer design Optical reconfigurable intelligent surfaces
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27.10.2025 Application deadline: 30.11.2025 Are you excited about the possibility to explore ethical, philosophical, legal, epistemic or social implications of using machine learning in different
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long-term partnerships to strengthen scientific capacity in the region. The candidate will also lead NYUAD’s annual Southeast Asia Learning Exchange, an immersive week long program that brings regional
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Postdoc (f/m/d) Leader of Junior Research Group "WEEE-Recycling" / Completed university studies (...
-hand experience in the application of machine learning, simulation and modelling concepts in resource technology # Proven track record of interdisciplinary collaboration along the value chain of raw
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measurements are most informative and guiding where, when and how to observe next. By combining Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more