325 data-"https:" "https:" "https:" "https:" "https:" "https:" "University of New Hampshire" positions at Monash University in Australia
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combination of multi-wavelength observational data with sophisticated simulations. I am a member of various collaborations, including Australia's OzGrav Centre of Excellence for Gravitational-wave Discovery
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explore unconventional ideas, develop computer algorithms for data analysis, create new experimental approaches, and apply the technique in areas like biomedicine, materials science, and geology. My group
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, and their decisions can be confusing due to brittleness, there is a critical need to understand their behaviour, analyse the (potential) failures of the models (or the data used to train them), debug
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area Software Engineering The objective of this project is to design automated approach to detect bugs in various software, e.g., compilers, data libraries and so on. The project may involve LLMs
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. This work combines computational modelling and simulation with biological experiments that are analysed using cutting-edge computer vision techniques. We collaborate closely with Macquarie University where
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known human reasoning difficulties and fallacies. It will also investigate how to reduce human cognitive load by prioritising the most useful information for the user. Expected outcomes include novel AI
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Despite the popularity of providing text analysis as a service by high-tech companies, it is still challenging to develop and deploy NLP applications involving sensitive and demographic information
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paradigms rely on a fragile "closed-world" assumption: that the unlabeled pool perfectly reflects the distribution of the labelled seed set. In real-world deployments, this is rarely true. Data streams
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The proliferation of misinformation and disinformation on online platforms has become a critical societal issue. The rapid spread of false information poses significant threats to public discourse
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weighted sum of the risks from tens to millions of independent disease-associated SNPs from across the genome. The conventional, or gold-standard, approach to analysis of GWAS data is to fit a regression