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data analysis pipelines using ImageJ, R, MATLAB and relevant programming languages. Plan, troubleshoot and optimise experiments, maintain meticulous records, and prepare data, manuscripts and grant
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collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in biologically-inspired deep learning and AI
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tasks involve the detection, discrimination, and interpretation of coloured signal lights and information presented on modern digital cab displays. The project will combine visual task analysis, spectral
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training programme at the start of the PhD to develop skills in areas such as programming, data analysis, machine learning and signal processing. This will provide the technical foundation required to work
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to games, it speaks to larger challenges in the museum sector. The programme of research is expected to encompass analysis of primary and secondary sources, qualitative research involving developers and
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semi-structured interviews, surveys, focus groups, analysis of existing data sets, and fieldwork with critical analysis, contributing both to academic debates and to practical understanding
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. This project will rely heavily on computational analyses, so experience on bioinformatics/informatics is required. The student will receive extensive training in big-data analysis, genome-wide/species-wide
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/structural/mechanical engineering with experience and interest in structural dynamics, vibrational analysis, train-track-bridge interaction, signal processing, data science and machine learning. The successful