452 web-programmer-developer-"https:"-"University-of-Cambridge"-"UCL"-"https:" positions at Monash University
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model with each SNP independently, perhaps adjusting for other covariates such as age and sex. This project will focus on developing and applying novel machine learning and AI methods to improve
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at prediction and pattern recognition tasks but still fails at very simple planning and decision-making problems. This project will develop predictive and prescriptive analytics algorithms that combine
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catastrophically so. This PhD will develop technologies for addressing this serious problem, building upon our groundbreaking research into the problem. Required knowledge A solid grounding in machine learning
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-term appointment Remuneration: The successful applicant will receive a tax-free stipend, at the current value of $36,063 per annum 2025 full-time rate, as per the Monash Research Training Program (RTP
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research community. This position is responsible for developing and maintaining a skilled team with strong technical expertise and a shared commitment to safe and ethical practice. It oversees quality
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for delivering high-quality lectures and tutorials, developing and updating course materials and academic research, conducting high-impact research in areas such as artificial intelligence, machine learning, cyber
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frameworks; develop the University’s long-term financial model; and embed best practice across budgeting, forecasting, scenario planning, research revenue reporting and analysis. This role will lead financial
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to reason with more than just and-gates, not-gates and or-gates! For example, we now have non-classical logics which capture notions such as “phi is true until psi becomes true”; “if we execute atomic program
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has enabled me to start investing, which is useful practical experience for me as a finance student. Through my scholarship program, I receive mentoring from alumni, which has helped me develop my
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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