314 data "https:" "https:" "https:" "https:" "Academia Sinica" positions at Monash University
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to real-life data. The goal is to generate new knowledge in the field of time series anomaly detection [1,2] through the invention of methods that effectively learn to generalise patterns of normal from
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techniques in bacterial genomics, including both short- (Illumina) and long-read sequencing (Oxford Nanopore), data mining of electronic medical records and use of machine learning to predict several outcomes
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Advisory System, or data from other implantable or wearable devices. This involves consideration of both feature-based machine learning or data science approaches and neural mass parameter estimation
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nutritional data into a user-friendly platform, enabling consumers, restaurants, and policymakers to make informed food choices and reduce diet-related emissions. Required knowledge Data analytics and software
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on clinical, genomics and functional dependency data (CRISPR, drug screens). Brain tumours represent the second most common cancer and the most common solid tumour in childhood in general. Paediatric brain
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motif, hence renders the identification of the binding protein difficult. Here we propose for the first time to apply the Bayesian information-theoretic Minimum Message Length (MML) principle to optimise
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back at least as far as 1954 (Dowe, 2008a, sec. 1, pp549-550). Discussion of how to do this using the Bayesian information-theoretic minimum message length (MML) approach (Wallace and Boulton, 1968
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to studying learners’ engagement with feedback have relied heavily on self-reported data. While informative, self-reported data can be susceptible to bias, poor memory, and incorrect self-assessment
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While deep learning has shown remarkable performance in medical imaging benchmarks, translating these results to real-world clinical deployment remains challenging. Models trained on data from one
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multiple teaching periods and campuses. This position will be responsible for assisting in the preparation and maintenance of assessment timetables, ensuring accurate data entry and calendar management