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Field
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reactions, catalyst surfaces and interfaces, reaction mechanisms, activity and selectivity develop reproducible atomistic simulation and high-throughput workflows using Python, ASE and relevant DFT software
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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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. You’ll bring strong technical capability across data extraction and transformation, visualisation, database querying and automation, with experience using tools such as Python and process mapping software
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requirements. To be successful in this position, you will have: A high-performing proactive team member who is adaptable to a fast-paced and ever-changing environment Experience coding in Python, SQL, R, Matlab
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related field. Strong analytical and manuscript preparation skills, experience analysing complex clinical or biological datasets and the ability to develop well-documented code in R, Python or another
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graphs or multi-agent reinforcement learning. Strong programming skills in Python and AI frameworks such as PyTorch, JAX or TensorFlow, combined with excellent analytical, problem-solving, communication
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effectively. Analytical proficiency using tools such as Excel, SPSS, DisplayR, Qualtrics, Alteryx, M, or Python. A proactive mindset with strong problem-solving skills and the ability to apply sound judgment
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using groundwater models and processing large datasets Demonstrated programming skills (e.g. MATLAB, Python, R). An understanding of and commitment to UNSW’s aims, objectives and values in action
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learning, materials characterisation or computational materials science. Previous experience with machine learning, computer vision, graph neural networks, Python, SEM/EBSD, XRD, image analysis
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STATA, R, SAS, PySpark and Python Previous experience in the management and analysis of linked health data, the production of statistical analysis plans and the use of GitHub Demonstrated ability