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. You will contribute to developing datasets, baseline models, personalized learning engines, reasoning-graph representations, cross-domain mapping algorithms, and RLHF-style feedback loops that improve
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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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the algorithm development team; to find relevant real-life benchmark tests, and to compare our developed methods against state-of-the-art. You will join a strong academic team at DTU Management’s Management
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homogenisation and energy group structure. Investigate the use of AI/ML algorithms to predict or generate cross sections, enabling deterministic solvers to better capture strong heterogeneities and flux gradients
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and Saeys teams. In this research project you will develop and apply algorithms to link clinical phenotypes of metastasis to molecular phenotypes in mouse models. It is known that metastases exhibit
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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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approach based on Deep Learning algorithms will be developed and implemented to obtain additional information by coupling the recorded data. Furthermore, the increase in acquisition rates of measurement
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, nonlinear dynamical systems, robotics, and formal methods to develop principled models and algorithms for distributed decision-making in complex and uncertain environments. Your research The candidate will
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. Expected Work: The selected candidate will conduct research at the intersection of wireless communications, signal processing, and control theory, aiming to develop cutting-edge algorithms for joint
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plus. You enjoy working with complex, multimodal datasets and developing robust algorithms for continuous monitoring and predictive modelling. You are comfortable combining coding, data analysis, and