327 data-"https:" "https:" "https:" "https:" "University of Cambridge" positions at Monash University
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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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Understanding factors related to student retention and experience in physics and astrophysics major units. Using quantitative (surveys) and qualitative data (interviews with students) this project
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I supervise computational projects in electron microscopy imaging for investigating materials at atomic resolution. Some projects centre on analysing experimental data acquired by experimental
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) to support surgeons, operating room technicians, and other professionals in and around operating room activities. Particular areas that may be explored are: Immersive OR analytics: using XR to analyse data
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existing tokenization frameworks, analyzing potential risks, and developing novel security protocols to protect sensitive data and ensure the integrity of tokenized assets. Applicants will investigate
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The United Nations Development Programme has identified access to information as an essential element to support poverty eradication. People living in poverty are often unable to access information
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environments—such as MOOCs, online degrees, and data-intensive Learning Management Systems—necessitate scalable solutions to provide timely, high-quality feedback. However, existing AI-powered assessment systems
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Multi-View Learning in CV and NLP Robust Active Learning Under Distribution Drift Data-Efficient Deep Learning for De Novo Molecular Design from Analytical Spectra Hybrid Quantum–Classical Algorithms
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Methods of balancing model complexity with goodness of fit include Akaike's information criterion (AIC), Schwarz's Bayesian information criterion (BIC), minimum description length (MDL) and minimum
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Minimum Message Length (MML) is an elegant information-theoretic framework for statistical inference and model selection developed by Chris Wallace and colleagues. The fundamental insight of MML is