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operations. The PhD will develop and implement artificial intelligence and data-driven methods for early anomaly detection, root cause diagnosis, and failure prediction on industrial systems, leveraging
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tuition fees. This PhD project in the area of autonomy, navigation and artificial intelligence, aims to advance the development of intelligent and resilient navigation systems for autonomous transport
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are: i) to develop a new selected CI algorithm allowing reaching chemically-accurate results for large compounds; ii) to extend the currently-available database to properties relevant for both core
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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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cellular level. They involve developing analytical methods and software tools for large, domain-specific, and heterogeneous data to understand cellular processes and apply findings in medicine and
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-and-egg" problem of sampling: developing algorithms that learn to bias simulations automatically without requiring prior knowledge of the mechanism. You will contribute to open-source software
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push the limits of multiphysics CFD for laser manufacturing by developing a next-generation simulation capability for laser drilling (with relevance to additive manufacturing). Your work will capture
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the likelihood of the target to fall within the stationary clutter returns and in the shadow of complex structures. We will investigate the use of multistatic radars against low observable threats and develop
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research. You will strengthen the data science and machine learning activities of IAS-9 by developing core AI methods with applications to electron microscopy and materials discovery. You will work in a team
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PhD working with Prof. Hendrik Blockeel and/or Prof. Jesse Davis on analysis of time series data. The goal is to develop algorithms for detecting anomalies, discovering “motifs” (repeating patterns