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tomorrow’s living environments? And how can we support mutual learning between researchers in the geo-information sciences and societal actors when engaging with an increasingly uncertain and turbulent future
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integrates machine learning and statistics to improve the efficiency and scalability of statistical algorithms. The project will develop innovative techniques to accelerate computational methods in uncertainty
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learning (ML) for high-fidelity data ‘stitching’. The integration of data from multiple analytical platforms is critical for advancing the understanding of complex biological and chemical systems. This work
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interdisciplinary lab and has faculty and students from multiple departments and colleges. The Director of the Brainwaves Research Laboratory, Dr. Davies, is a faculty member in the Department of Occupational Therapy
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for greater precision. Machine learning (ML) algorithms will analyse these datasets to deliver a scalable, cost-effective system, validated through field trials and enhanced by contributions from four
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NMBU. The applicant must have an academically relevant education corresponding to a five-year master’s degree, with a learning outcome corresponding to the descriptions in the Norwegian Qualification
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used to develop networks capable of self-learning and self-optimisation, adapting to real-time changes in traffic and demand. The successful candidate will contribute to designing solutions that optimise
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university research into commercial outcomes. Under this program, PhD students will gain unique skills to focus on impact-driven research. This Project aims to develop a predictive machine learning model
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techniques to combine data from multiple sources, integrating both visual and textual information to deepen our understanding of patient health. This studentship offers a unique opportunity to contribute
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MSc/PhD Position at the Faculty of Medicine, Memorial University of Newfoundland, St. John’s, Canada
research project focused on exploring the immunometabolic pathways of macrophages and microglia in multiple sclerosis (MS). The Kaushik Lab is committed to fostering an inclusive and equitable research