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, including machine learning approaches to transcribing, re-structuring and analysing archival collections. The PhD student employed will be part of an award-winning team of computational social historians
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student in this project, you will contribute to the development of new models and methods in machine learning for D-MIMO integrated sensing. This includes working with large amounts of data generated by a
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the "Machine Learning and Gene Regulation" team led by William Ritchie, specializing in bioinformatics and post-transcriptional regulation. The scientific environment at the IGH — international seminars, journal
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diagnosis and prognosis technologies, and, consequently, improve maintenance decision making. Currently, machine learning exists as the most promising technologies of big data analytics in industrial problems
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to the development of new models and methods in machine learning for D-MIMO integrated sensing. This includes working with large amounts of data generated by a unique D-MIMO testbed at Lund University, extending
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academic writing (e.g., previous conference or journal articles) Personal qualities Be highly motivated for completing a PhD Be open-minded and eager to learn Be goal-oriented, accurate, analytical and
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application! We are looking for a PhD student for sustainable and resource-efficient machine learning. Your work assignments Machine learning has recently advanced through scaling model sizes, training budgets
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processing, and machine learning techniques is considered an advantage. We are looking for a motivated, proactive, and curious PhD candidate that enjoys working across disciplines and contributing to a shared
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collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in biologically-inspired deep learning and AI
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research. Experience applying machine learning and statistical modeling techniques to large biological datasets for biomarker discovery, disease prediction, or host-pathogen investigations. Proficiency in