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Field
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languages such as Python, Go, Rust or C. Containerisation technologies including Docker and Kubernetes, along with associated runtimes such as containerd and runc. Cloud-native technologies, distributed
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-scale implementations. Exposure to JavaScript, HMTL, CSS, Python, SQL and RESTful APIs preferred. Hands-on experience with data management, audience segmentation and personalisation strategies. High-level
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experience, deemed equivalent by the GRC (or delegate). The ideal PhD candidate will have: A strong background in machine learning, deep learning, and signal processing Proficiency in Python and machine
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should ideally have experience in: Essential Deep learning and machine learning Computer vision Python programming PyTorch or TensorFlow Strong mathematical and analytical skills Desirable Video
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. Advanced quantitative programming and data-analysis skills, preferably in Python, together with the ability to develop transparent, robust and reproducible modelling workflows. Experience working with large
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, including publications record in a relevant field strong programming and computational research skills, such as Python, R, Julia, MATLAB, R, C/C++, or equivalent research expertise in one or more of the
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developing code to perform the data operations including extraction, cleansing and transformation. · extensive experience in SQL and Python is required, other skills will be considered
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-quality research appropriate to career stage evidence of a developing research profile, including publications record in a relevant field strong programming and computational research skills, such as Python
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development in data engineering, data systems, or related areas. Experience in database platforms, systems software, or data infrastructure development using languages such as C/C++, Java, Rust, Go, or Python
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-quality research appropriate to career stage evidence of a developing research profile, including publications record in a relevant field strong programming and computational research skills, such as Python