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Vacancies PhD position on Dependability Driven on Device Learning Algorithms for Embedded Neuromorphic Architectures Key takeaways Edge devices that can learn autonomously while guaranteeing
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a variety of machine learning algorithms trained on these data and, most crucially, will develop and implement techniques for computing the uncertainty in the prediction. The algorithms developed in
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algorithms to detect complex structural variants in humans using long DNA sequencing reads. A structural variant (SV) is a large-scale alteration in the genome that involves rearranged, deleted, or inserted
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a variety of machine learning algorithms trained on these data and, most crucially, will develop and implement techniques for computing the uncertainty in the prediction. The algorithms developed in
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experts in smart cities, artificial intelligence, transportation planning, and urban sustainability. We are seeking applicants interested in developing an efficient system’s approach for optimising urban
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, both in the classical and parameterized setting. The goal is to develop general tools that can provide efficient algorithms for a wide range of graph modification problems. Find more general information
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. Candidates must have a strong motivation for research and excellent programming skills. Expertise of developing computer vision and machine learning algorithms would be desirable, with an interest in image
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is to develop a highly innovative ‘Lab on a Bench (LoB)’ setup, integrated with Machine Learning algorithm, as a high throughput method for screening and developing formulated products that are used in
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Join our multidisciplinary research team to develop and apply machine learning and bioinformatic algorithms in biomedical research. This PhD project will focus on developing machine learning
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supervised by Prof. Dr. Roger Wattenhofer and Dr. Antonio Di Maio . You will be entrusted with designing, developing, and evaluating data-driven methods, algorithms, and systems for three independent but