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Required knowledge Skills Focus proficiency in one programming language (e.g. Matlab, R, Python), machine learning / deep learning / data science skills, basic understanding of cell and development
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learning or a related field experience in the development of machine learning models using Python and pytorch expertise in two or more of the following technical areas: implementation of signal processing
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Python scripts, calibrate specialised instrumentation and solve technical challenges that directly support researchers conducting experiments in a unique outdoor laboratory. Working alongside researchers
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such as SPSS, R or Python; Power BI or equivalent a strong advantage. Demonstrated experience designing and validating quantitative analyses and models, with a focus on rigour, accuracy and reproducibility
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ranked among the top engineering faculties worldwide. For more information, visit the Faculty website: https://www.unsw.edu.au/engineering/about-us Skills & Experience: A PhD in a related discipline, and
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estimation. Experience with convex or semidefinite optimisation, variational methods, or computational complexity. Proficiency in relevant scientific programming or quantum-software tools, such as Python
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service needs, applying human-centred design approaches to shape intuitive, accessible and scalable digital products, predominantly using Node.js, Svelte, React and supporting Python services where
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deep hands‑on expertise in advanced analytics, machine learning and production‑level data science, alongside strong capability in SQL, Python or R, and modern cloud data platforms. Unlike a Data
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solutions using tools such as R and Python, transform complex datasets into meaningful insights, and communicate findings to technical and non-technical audiences. Working across research, industry and
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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