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Field
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, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with causal machine learning, ensemble methods, and deep learning
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, and finally using deep learning to solve the complexity challenge associated with coherent beam combination. The role Within HiPPo, your specific task will be to develop a ‘digital fibre laser’, through
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, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with causal machine learning, ensemble methods, and deep learning
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Engineering, Mechatronics, Computer Science, etc. Strong background in AI, Vision Language Model, end-to-end autonomous driving, deep learning, computer vision, robotics and automation. Candidates having
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preferred. Background with pharmacology, cell therapy or antibody engineering is also desired. · Strong experience in induced pluripotent stem cell (iPSC) establishment possesses a deep understanding
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communication skills. Proficiency in developing deep learning models using frameworks such as PyTorch and TensorFlow. Research experience in medical image analysis using deep learning algorithms. Strong track record in
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enrichment (GO, KEGG), network analysis, genome assembly and binning, systems biology, and multi-omics integration. Apply statistical modelling, machine learning, and deep learning approaches for biomarker
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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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and management, machine and deep learning, as well as a solid understanding of wave phenomena and geophysical data analysis and imaging.
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; distributionally robust optimization; 2) Graph Neural Networks, Large Language Models (LLMs), and geometric deep learning; and 3) federated learning and privacy preserving computing. Basic Qualifications Candidates