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Optimal sampling and control of spatial populations in real time

Note: There is a top-up scholarship funded by CSIRO available for this project for one PhD student who holds a QUT scholarship, an Australian Government Research Training Program (RTP) scholarship, or some similar scholarship. This top-up provides additional stipend ($7,000 per annum) and generous support costs (including travel).Animals do not follow simple paths when they move through heterogeneous landscapes, so modelling and managing their populations is a complex mathematical problem. In order to optimise planning of population management, we must …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
Lead unit
School of Mathematical Sciences

Central Queensland Coal Chain Optimisation

The Central Queensland Coal Chain (CQCC) is a complex system with many stakeholders and varying objectives. The lack of coordinated planning has led to inefficiencies throughout the entire system, most notably congestion in coal ports.

Study level
PhD, Master of Philosophy
Faculty
Science and Engineering Faculty
Lead unit
School of Mathematical Sciences

Analysis of quantum bayesian networks under a real-world medical decision problem

Probabilistic graphical models, such as Bayesian Networks, are one of the most powerful structures known by the Computer Science community for deriving inferences based on probability.However, modern cognitive psychology has revealed that human decisions could not follow the rules of classical probability theory because humans cannot process large amounts of data in order to make judgements. Consequently, the inferences performed are based on limited data coupled with several heuristics, leading to violations of the law of total probability. This means …

Study level
Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
Lead unit
School of Information Systems

Turning predictions into decisions: innovation to facilitate spatio-temporal decision-making in the agricultural sciences

As agriculture meets the digital age, we are faced with challenging decisions about when to sow, when and how much to fertilise and when to irrigate. With the aid of high performance computing, models that represent complex agricultural processes can be used to simulate a wide range of farming scenarios in space and through time. Coupled with other sources of information (e.g. measured data and expert information) the challenge becomes being able to quantify the uncertainties and interpret the outputs …

Study level
PhD
Faculty
Science and Engineering Faculty
Lead unit
School of Mathematical Sciences

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