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motivated candidate with a strong background in statistics and/or machine learning. Areas of particular interest include, but are not limited to: Causal Discovery and Causal Inference Extreme Value Theory
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molecular docking, molecular dynamics and free-energy methods (MD/FEP), machine learning for molecular design, and protein–ligand modelling. Experience bridging computational and experimental groups, and the
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(DInSAR). Minute surface uplift and subsidence signals will be automatically detected using machine-learning workflows, enabling systematic, user-independent identification of drainage events every 6–12
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to these uses cases by developing machine-learning based methods for analyzing IoT time-series data. Your Competencies: We seek a highly motivated researcher with a PhD in computer science, proficiency in
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Professorhip grant, which you can learn more about here: https://www.cnap.hst.aau.dk/lundbeck-professorship As a PhD fellow your tasks include: Conduct research under the supervision of senior CNAP staff members
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Systems at The Technical Faculty of IT and Design invites applications for PhD stipends or integrated stipends in the field of Machine Learning for Intelligent Hearing Assistance in Complex Acoustic
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that aims to redesign how students learn programming through AI-driven, dialogue based, and pedagogically grounded tools. The PhD candidate will contribute to a cross-faculty collaboration spanning the TECH
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machine learning methods for detecting, classifying, and identifying wireless anomalies in real-world radio environments. You will design and experiment with AI-driven approaches for spectrum analysis, work
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machine learning models directly on these edge devices for real-time anomaly detection and identification. You will develop robust signal acquisition and processing pipelines, translate research-grade
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of this PhD project is to develop machine learning algorithms that perform efficiently and coherently across both classical and quantum computing platforms. The PhD project falls under the collaboration between