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Field
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learning by 2028. Our strategic research purpose is to create life-changing and world-leading research for the wellbeing of our people, place and planet. About the Fellowship: Established in 2025 by the Vice
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models • Strong experience in spatio-temporal deep learning and ensemble techniques • Proven ability to publish research in international peer-reviewed journals • Experience with interdisciplinary
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. Machine Learning Expertise: Familiarity with causal machine learning, ensemble methods, and deep learning architectures (CNNs, RNNs). Experience with explainable AI (e.g., SHAP, LIME) is preferred
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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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Sensing (DAS) data processing and compression using ML Physics-driven machine learning for geophysical modeling and inversion Thus, the candidate is expected to have or about to have a PhD in a relevant
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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
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research, formulate hypotheses, and design effective experimental plans. Strong programming skills with deep learning frameworks (e.g., PyTorch). We regret that only shortlisted candidates will be notified
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deep learning (segmentation and foundation-model architectures) Demonstrated ability to design and execute technical projects Scientific independence with clear interest in biological mechanisms and
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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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, applying deep domain knowledge and advanced quantitative methods to inform critical development decisions. At Northeastern University, the Fellows will engage in scientific publication, conference