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active research interests in quantum computing, quantum algorithm/software development and applications in decarbonisation who can take full advantage of the unique opportunities the QDA provides
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algorithms from medical records, improving Astronaut’s medical monitoring during missions and throughout their career; Develop automated workflow for data extraction and import; Support development
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for the Future of Intelligence (CFI). The candidate will contribute to teaching and lead a research programme in one or more of the following areas: ethical use of machine learning, AI safety, algorithmic
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distributed tasks such as inspection of a big geographic area for fires is an important objective of this project. The project aims are to: 1) provide methodological contributions towards intelligent sensing
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out research on distributed acoustic sensing (DAS) data analysis and participate in continued development on the inclusion of DAS data within the network monitoring systems. We seek applicants who like
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with MoniRail Ltd and Durham University. The primary focus will be on designing and implementing deep learning and anomaly detection algorithms to analyse large-scale, real-world sensor data collected
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Status: Nonexempt Work Schedule: Flexible Summary The intern will receive training on machine learning algorithms, implementing them, and fitting them on the datasets in our cluster computers. Overall
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that engender trust and espouse the values of fairness, safety, and privacy in digital technologies. Key Responsibilities: Conduct research into trust technologies testing – translating research algorithms, and
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computational tools and algorithms developed in the lab for processing and visualizing proteomics data (known as FragPipe computational platform). The individual will work in close collaboration with other
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this short-term project we will use active learning to accelerate the training of deep learning algorithm to optimize 2D material Van der Waals structure discovery. Main Tasks and responsibilities: The person