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-loss events undermine statistical confidence. The aim is to develop i) edge intelligence (on-turbine smart algorithms for data preprocessing), ii) resilient data movement (error-tolerant, cybersecure
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visions of AI-native data systems capable of querying unstructured data declaratively, you will design and evaluate novel algorithms for extracting entities and relationships, while capturing provenance and
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, computer science, and statistics The objective of this PhD project is to develop machine learning algorithms that perform efficiently and coherently across both classical and quantum computing platforms. The PhD
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models to design adaptive, efficient, and intelligent algorithms for hearing assistive devices. Key objectives include improving speech perception in noisy and unpredictable environments, reducing
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. The PhD students will work on several tasks, including: development of safe data-driven control/reinforcement learning algorithms to recover parameter identifiability by exploration of different
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correction and/or mitigation. Knowledge about networking protocols and distributed algorithms. Experience in programming, e.g., in C++, Python or Matlab. Experience with quantum simulators, such as NetSquid
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sensor integration. Experience with SLAM algorithms (vision-, acoustic-, or inertial-based), state estimation (e.g. Kalman filtering, pose graph optimization), or collaborative positioning is highly valued
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deliver a theoretical, algorithmic, and real-time implementation framework for on-the-fly autonomy in crowds. The resulting methods will (i) adapt to unpredictable human interactions that introduce high
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algorithmic solution development. The group focuses particularly on automated decision-making in autonomous cyber-physical systems, combining mathematical optimization, machine learning, and decision theory