238 data-"https:"-"https:"-"https:"-"https:" uni jobs at Monash University in Australia
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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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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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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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-disciplinary team of clinician scientists and computer scientists to develop diagnosis/predictive/treatment/robotics surgery models of diseases of interest using multimodal medical data, consisting of images
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extract events and mine knowledge from existing unstructured/structured data, and exploit the knowledge via neuro-symbolic reasoning for crime prevention (eg -sexual assaults), especially when there is no
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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