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unappealing or suboptimal coverage results. In this thesis, you will first analyze the variety of expected environments and the specific requirements placed on the algorithm. From this, you will evaluate
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complex nonlinear models, with unique research opportunities in algorithm and hardware co-design, particularly for resource-constrained environments. Applications focus on edge intelligence
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Scaling analog in-memory computing towards large generative AI models H/F Subject Your work will span device-to-algorithm co-design, focusing on state-of-the-art small language model engineering and
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(HPC) facilities at QUT. PhD2: Pore-network modelling of reactive transport As a PhD student, you will develop efficient pore-network modelling algorithm that allows accurate simulation of fluid
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knowledge and experience in computing, algorithm development, computer simulation, and/or artificial intelligence and machine learning; has excellent communication skills and a demonstrated ability to
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Algorithm (2025). Engineering , DOI: 10.1016/j.eng.2025.04.023. [2] Zheng, Z., Smirnova, D., Sanderson, G., Ying, C., Koutsogeorgis, D. C., Huang, L., Liu, Z., Oulton, R., Yousefi, A., Miroshnichenko
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, Position 2 on adaptive policies, and both will work jointly to integrate the two approaches into a single RL algorithm. While the work is mainly methodological and theoretically focused, our Aalto robot lab
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and GWAS methodology Machine learning approaches for high-dimensional data Algorithm development and evaluation Applications of AI in biomedical research This project is suitable for students with an
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and GWAS methodology Machine learning approaches for high-dimensional data Algorithm development and evaluation Applications of AI in biomedical research This project is suitable for students with an
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-standard tools such as Matlab Simulink to connect with existing control systems and support SIL/MIL simulation, and the resulting algorithm must run efficiently within the CPU constraints of an embedded