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This project develops quantum-AI tools to optimise urban hydroponic food systems and evaluate their therapeutic impact on student well-being. The expected outcome is to create quantum-reinforced AI
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fabrication facilities as well as high performance computing (HPC) facilities at QUT. PhD2: Pore-network modelling of reactive transport As a PhD student, you will develop efficient pore-network modelling
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FOR DRAWING UP OF PREDOCTORAL CONTRACTS FOR THE TRAINING OF DOCTORAL STUDENTS FUNDED BY THE UPV'S RESEARCH STRUCTURES – SUBPROGRAMME 2 (PAID-01-22) 119865 Development of machine-learning and graph-based models
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position (technician) will focus on performing Raman/FTIR on retrieved samples. The PhD position will focus on developing a deep-learning algorithm for analyzing the acquired experimental data.
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to develop Computer Vision algorithm and Interfaces for collaborations with Historians teams. Specifically, the research will focus on structured data with clear repeated patterns, such as characters in a text
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will include race videos, rider power and speed data, and race commentary to codify key race events, using expert knowledge and available evidence. - Develop a post-race analysis framework, process, and
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research on the development of new inference methods and algorithms for wide classes of stochastic models. However, research will be conducted in collaboration with biologically oriented researches allowing
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adaptation of the mesh during simulation to resolve and track features in the flow. The focus of your PhD would be on developing novel algorithms to efficiently redistribute and rebalance the parallel
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for ground-penetrating radar (GPR) and electromagnetic (EM) will be developed. These algorithms will enable high-resolution, quantitative time-lapse soil property measurements using high-performance, parallel
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STUDENTS FUNDED BY THE UPV'S RESEARCH STRUCTURES – SUBPROGRAMME 2 (PAID-01-22) 119977 Development of mathematical and machine-learning algorithms to support an intelligent, integrated system for biosafety