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Field
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data sets using custom MatLab or Python codes, undertake data analysis using clustering and waveform techniques within MatLab or Python with support from research team Presentation of results: including
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systems, ideally including mobile manipulators. You will be proficient in software development (Java, Python, C++), with object-oriented programming and software engineering practices. You will have hands
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modelling; strong programming and research engineering ability (Python with modern ML tooling such as PyTorch/JAX) and good software practices; familiarity with interactive/agentic settings (e.g., tool-using
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will lead the programming of R/Python packages for the analysis as well as adapt existing and develop new research methodologies and training materials. You will report research findings in the form
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computing skills, including the knowledge of UNIX/Linux, Fortran, Python, or other high-level languages. The post is full time and fixed term until 31 December 2026. The closing date for applications is noon
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discipline and will bring strong programming skills (for example in Python), alongside experience working with large-scale machine learning models. Prior experience with healthcare data and a strong
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or Python. With desirable skills include experience with culturing unicellular eukaryotes and comparative genomics. You will hold strong communication skills and effective collaboration, with demonstrable
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, sufficient programming fluency (e.g., Python; familiarity with common ML tooling) to run computational experiments, and excellent communication skills, including writing for publication and presenting results
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modelling; strong programming and research engineering ability (Python with modern ML tooling such as PyTorch/JAX) and good software practices; familiarity with interactive/agentic settings (e.g., tool-using
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programming skills, Python frameworks for ML and real-time audio software • Curating audio datasets, spatial-audio/audio-visual recordings, and ML training/evaluation pipelines • Experimental systems