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Field
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required Demonstrated expertise with large language models (fine-tuning, prompting, deployment) Strong Python programming with deep learning frameworks (PyTorch, TensorFlow) Experience with unstructured
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recombinant minibinders for migraine-associated receptors. The project aims to advance deep learning–based molecular generation and structure-guided design for therapeutic innovation. We seek a highly motivated
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modeling; pharmacogenomics; health informatics; data science, machine learning, or deep learning; causal inference methods Strong written and oral communication skills, with at least one first-author
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for Machine Learning (AIML) is the largest university‑based machine learning research group in Australia and the country’s first institute dedicated to advancing machine learning, computer vision, deep learning
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(but are not limited to) Computer Science, statistics, mathematics, automation, informatics, and Engineering. Experience in deep learning, machine learning and medical imaging processing Programming
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Earth Observation data analysis and/or spatial modeling Proven ability to publish in high impact peer-reviewed international journals Experience with machine/deep learning / AI applied to environmental
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documented expertise in: Modeling and simulation of physical systems, Deep learning with applications in robotics, in particular field robotics, and Control and motion planning of mechatronic systems
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Engineering, Medical Image Analysis, Applied Mathematics or a related field Experience with deep learning for image analysis, preferably in medical imaging Experience with generative modelling, ideally
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and deploy advanced deep learning and foundation models for surgical scene understanding segmentation, tracking, and operator assistance. You will write, test, and optimise Python and C++ code for real
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approaches to remove atmospheric particulate (e.g., PM2.5) pollution. The math-based subgroup focuses on the use of deep learning and generative AI to address critical problems for the electric grid and broad