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into real-world settings. You will be responsible for developing machine learning and AI algorithms for a range of data and applications (e.g. natural language processing, multivariate time-series data
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construction machinery to improve efficiency, adaptability, and safety under varying operating conditions. The work will involve designing and prototyping intelligent control algorithms, developing runtime
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Project (Next-Generation Solvers for Complex Microwave Engineering Problems). This project aims to design and develop physics-guided, data-driven algorithms that can accurately solve complex microwave
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mix of algorithm engineering and formal methods, alongside more traditional software engineering activities. This project involves developing software which is both mathematically rigorous, and
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candidate will design and develop digital twin algorithms for computational fluid dynamics, contributing to innovative solutions in healthcare software systems. This role involves collaboration with research
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experimentation with Asst. Prof. Eli N. Weinstein. Your goal will be to develop fundamental algorithmic techniques to overcome critical bottlenecks on data scale and quality, enabling scientists to gather vastly
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quantitative analysis skills and experience developing algorithms and/or conducting statistical analyses with biological datasets. Background and work knowledge in statistics, algorithms, optimization of novel
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learning, and data science, focusing on applications within the healthcare, education, and environment sectors. Designs generative AI techniques and algorithms for data integration and computational models
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Future-Proof Smart Logistics. It aims to contribute to the realisation of the PI concept by developing advanced machine learning-based decentralised decision-making algorithms. These algorithms will enable
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-resource settings. This project aims to achieve several objectives, including the development of a new AI-algorithm and a paired dataset for comparing how different imaging techniques influence