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for real-world deployment. Key Responsibilities: Develop algorithms for DAS signal processing, wavefield analysis, feature extraction, and interpretation across infrastructure monitoring, urban geophysics
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quantum matter and quantum computation. Our research includes (1) AI for Quantum: developing advanced AI algorithms grounded in physical principles and machine learning theory to tackle key challenges in
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monitoring including geohazard detection such as sinkhole and subsidence. Key Responsibilities: Develop algorithms for passive seismology, seismic noise analysis, geophysical imaging, and seismic source
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identification through lab-scale and field experiments. Key Responsibilities: Develop algorithms for guided-wave analysis, response analysis, sensor fusion, and system identification using distributed and multi
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for tumor behavior and clinical outcomes Development and implementation of artificial intelligence and machine learning algorithms for biologically and clinically motivated questions in pediatric oncology
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development of explainable and interpretable classical and quantum deep learning algorithms - Design and development of hackathons and other collaborative methodologies to promote the control of artificial
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. The tasks include battery cell characterization and modelling based on laboratory tests, and development of algorithms for estimating the charge level, health, and power capability which includes robustness
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on developing biofluid biomarkers that improve diagnosis, define disease heterogeneity, and can be implicated in clinical trials. A major complementary effort in the lab focuses on advanced
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. The successful candidate will assist in developing novel algorithms and integrating them into robotic platforms, helping to push the boundaries of embodied intelligence in both research and practical deployment
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(the final scope will be developed together with the candidate, and it is not necessary to cover all topics): Group fairness: developing and analysing algorithms and metrics for ensuring fair treatment