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DNA or RNA motif discovery is a popular biological method to identify over-represented DNA or RNA sequences in next generation sequencing experiments. These motifs represent the binding site of transcription factors or RNA-binding proteins. DNA or RNA binding sites are often variable. However,...
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In NeuroDistSys (NDS): Optimized Distributed Training and Inference on Large-Scale Distributed Systems, we aim to design and implement cutting-edge techniques to optimize the training and inference
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of Machine Learning as the problem of approximating function f from the pair of measurements (x,y), and Optimization as the problem of finding the value of input x that maximizes the output y given
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Explore the asymptotic behavior of MML estimators, including consistency, convergence rates, and their connection to information geometry. Investigate how the optimal data space partitioning relates
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the development of a suite of interventions for increasing digital literacy and wellbeing amongst older adult workers and their clients as part of the ARC ITTC Optimal Ageing project; and the development
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on social dilemmas, i.e., situations where poor group outcomes arise from optimal individual choices. We use this framework to study: Multi-agent Systems and AI, Social Systems, and Models in Biology and
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modelling framework to develop new understandings Full time, four-years fixed-term, based in Hobart About the opportunity This postdoctoral research position will develop and apply an optimal spatial
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management expertise Ensure alignment with architectural standards and business process optimization The Opportunity Drive a high-impact transformation across global campuses. We are seeking an experienced
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School is inviting you to join us as a Research Nurse (Oncology) to coordinate clinical trials, ensuring top-quality data and optimal participant care. Your role includes recruitment management, nursing
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of the AAAI Conference on Artificial Intelligence (Vol. 26, No. 1, pp. 267-273). - Blau, T., Bonilla, E. V., Chades, I., & Dezfouli, A. (2022, June). Optimizing sequential experimental design with deep