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at the John Curtin School of Medical Research (JCSMR), within the ANU College of Science and Medicine. This role will lead the development of advanced deep learning frameworks—including graph neural networks
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. Research areas include Representation Learning, Machine learning and Optimization on graphs and manifolds, as well as applications of geometric methods in the Sciences. This is a one-year position with
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learning architectures including generative models, particularly for sequence or structural data (e.g. transformers, graph neural networks, diffusion models) Proved experience in working independently and as
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learning architectures including generative models, particularly for sequence or structural data (e.g. transformers, graph neural networks, diffusion models) Proved experience in working independently and as
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, particularly for sequence or structural data (e.g. transformers, graph neural networks, diffusion models) Proved experience in working independently and as part of a multidisciplinary team Evidence of strong
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the following mandatory requirements: a) A completed degree in Chemical and Biological Engineering; b) Good knowledge in the areas of Machine Learning, Microbiology, Knowledge Graphs, and Language
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. Proven expertise in AI/ML for systems or hardware co-design, including use of reinforcement learning, LLMs, graph-based optimization, or agentic AI. Familiarity with security concepts and cryptographic
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receipts of proposals, and maintaining a system to track proposals. Evaluate and perform preliminary analysis of the data using graphs, charts or tables to highlight the key points of the research results
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Jhunjhunwala Lab at Genentech, please go to: https://www.gene.com/scientists/our-scientists/suchit-jhunjhunwala Relevant publications: Thrift, W. J. et al. Graph-pMHC: graph neural network approach to MHC class
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various sets of data to prepare databases for cross-disciplinary AI research and learning. Incorporates open knowledge graph networks. Takes the lead in drafting scientific papers and technical documents