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by: Developing specialized algorithms supported on solid theoretical foundations and with a focus on challenging aspects of very high-dimensional datasets, such as datasets encountered in
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Supervisors: Prof. Finn Werner – Werner Lab Website Dr. Christopher Waudby – Waudby Lab Website Abstract: RNA polymerases (RNAPs) are essential enzymes for viral replication and represent
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SecureMail Portal https://securemail.tu-dresden.de by sending it as a single pdf file to kerstin.achtruth at tu-dresden.de or to: TU Dresden, Chair of Algorithms, Prof. Dr. László Kozma, Helmholtzstr. 10
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transformed not only how we read, but also how we learn to read, bringing both new challenges and exciting opportunities. To fully understand these developments, this project explores the processes underlying
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programming skills. Expertise in developing computer vision and machine learning algorithms would be desirable, highly motivated and enthusiastic about advancing AI for societal impact. Qualifications A high
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in the network. Here unfair indicates that people with different personal traits are differently and unjustly affected by algorithms not designed to consider those traits. This project aims to develop
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-resolution wearable sensor streams, and endocrine test outcomes. Intelligent Artifact Detection: Develop cutting-edge Machine Learning algorithms to automatically identify, flag, and mitigate data artifacts
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edge of energy systems and computational engineering, developing scalable methods to simulate and secure IBR-dominated grids. Your key responsibilities include: Conducting large-scale simulations
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Research Studentship in Neural Engineering 3.5-year D.Phil. studentship Project: Sleep classification for implanted neurostimulation systems Supervisors: Dr. Joram van Rheede, Prof. Timothy Denison
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to seamlessly integrate complex hormonal data, high-resolution wearable sensor streams, and endocrine test outcomes. Intelligent Artifact Detection: Develop cutting-edge Machine Learning algorithms