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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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, bringing together emerging talent and senior researchers with decades of pioneering contributions. The group develops cutting-edge algorithms, frameworks, and prototype technologies that leverage high-volume
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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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about the department at www.es.aau.dk . Description of the position The position focuses on developing next-generation learning-based decision-making and control for autonomous robots operating safely in
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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