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Economics, to develop next-generation analytic tools that will illuminate the lived experience and care pathways of people with Lewy Body dementia (LBD). Each studentship is fully funded (stipend €25,000 p.a
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models. Through the use of decentralized edge computing, cloud-based analytics, and existing seismic networks, the project aims to improve rapid decision-making and resilience assessment during and after
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computationally efficient numerical structural models. To support the condition (state) assessment, the project will also explore the use of advanced estimators (e.g., Kalman Filter) or Machine Learning models
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operational data and machine learning. You will be based at UCL mechanical Engineering, and collaborate with industry and port partners on system design, prototyping, and lab-based trials. Key responsibilities
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of Unix systems (GNU Linux) and keen to gain hands-on experience in Networks and systems Machine Learning knowledge is a plus Strong analytical and programming skills are required (Python, Matlab, Golang
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. With over 26,000 students and more than 4000 scientists, research, teaching, and learning dedicated to the advancement of science and technology have been conducted here for more than 200 years, guided
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a focus in economics, or related disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation
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of innovative computational methods using Big Data, Behavioural Science and Machine Learning to understand behaviour through the lens of digital footprint/“smart data” datasets, cutting across sectors ranging
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data handling Strong self-initiative and willingness to learn and to quickly gain an understanding of new subject areas Joy in working independently, as well as within a team Strong problem-solving
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Apply now Are you interested in helping to realise long-term sustainable futures and solve short-term challenges? Do you have a keen analytical eye as well as the social skills to connect people? Are you