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for entry into a PhD program. A background in machine learning, inverse problems, scientific computing, or related data-driven methods is highly desirable. You are curious about combining physical modeling
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science, as well as PhD education in computer technology. The Mohns Center for Innovation and Regional Development researches innovation and offers master's education in innovation and entrepreneurship
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Responsibilities: Conduct project related research on geophysical/geomechanical investigation, geomodelling, data analytics, and machine learning. Responsible for processing and analyzing geophysical/geomechanical
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/nanoplastics, and other environmental compounds to assess their potential impacts on human health and the environment using machine learning (ML), deep learning (DL), and big data analytics. His lab is
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statistical and machine learning methods applied to large claims and electronic health record databases and multimodal data, including physiological waveforms and medical imaging. We foster a collaborative and
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and analysis of dedicated algorithms for training analog circuits directly from data. In this PhD project, you will develop a novel system-theoretic framework for learning in analog circuits and
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literature. Main responsibilities • Design, implement and optimize signal-processing, statistical and machine-learning algorithms for eye-movement (oculomotor) and voice/speech data. • Validate
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BMS constraints. Experience with system identification, uncertainty-aware modelling, large datasets, and machine learning. Evidence of research capability through a thesis, publications, conference
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socioeconomic registry data, biobanks and patient-reported data. Using advanced epidemiological methods, causal inference and machine learning techniques, we aim to: Improve understanding of risk factors for
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develop techniques to tackle the dynamics of these systems both in imaginary and real time. The latter will form a quantum inspired quantum computer emulator that should be able to tackle large systems