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Field
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. Qualifications The applicants should possess: An excellent or very-good university degree in economics, business studies, agricultural sciences with a focus in economics, or related disciplines Strong analytical
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synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application to the analysis of time series. In particular, the project
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disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with
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untapped potential remains in extracting value from this data. This PhD will explore advanced analytics techniques, including machine learning, digital twin modelling, time series analysis, spectral analysis
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: excellent, very good or good university degree (diploma, master's degree) in transport or related study programs with a solid basis in transport planning and/or data analytics Description of the PhD topic
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development (using both traditional signal processing and machine learning), antenna design, and system hardware development. We collaborate closely with clinical experts to develop innovative technologies
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learning (ML) for high-fidelity data ‘stitching’. The integration of data from multiple analytical platforms is critical for advancing the understanding of complex biological and chemical systems. This work
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identify novel drug targets. They will be trained in a variety of powerful, modern analytic techniques including metabolomics, transcriptomics, and proteomics and work on advanced biological systems
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Human Behaviour in projects targeting social good. Research at N/LAB focuses on the development and application of innovative computational methods using Big Data, Behavioural Science and Machine Learning
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Primary supervisor - Prof Kate Kemsley Join us to research and develop advanced analytical methods for tackling food fraud head-on! Economically motivated adulteration of foods is a significant