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on Artificial Neural Networks and Gaussian Process modelling, to accelerate processing optimisation. Consolidate experimental, techno‑economic, and sustainability data into robust technical evidence packages
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, Bayesian inference, model calibration, and Markov Chain Monte Carlo methods, uncertainty quantification, statistical modelling, and Gaussian processes, machine learning for time series, sequence-to-sequence
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analyze quantum chemical and molecular dynamics calculations Develop and apply machine learning models to chemical and spectrometric data Process, interpret, and visualize computational and experimental
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, industrial processes often require deeper object knowledge beyond geometrical dimensions. Color, roughness, reflectance behavior, and many other optically detectable material properties carry essential
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Job Description Model Development: • Develop and train surrogate ML models (e.g., neural networks, Gaussian processes, gradient boosting) to emulate computationally intensive building and urban
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(NUS), conducts cutting-edge research at the intersection of Augmented, Virtual, and Mixed Reality (AR/VR/MR), Artificial Intelligence (AI), and Human-Computer Interaction (HCI). The lab has a strong
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difficult to access on near-term quantum hardware. The difficulty is that the relevant Hilbert spaces grow rapidly, the dynamics are non-Gaussian and nonlinear, and the circuits must be mapped carefully
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of settings where only parts of a system are extreme. Special attention will be given to parametric families like the Hüsler–Reiss distribution, which lead to an extremal analogue of Gaussian structural causal
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interactions to neutron-rich systems. The central aim of this project is to develop a Gaussian Process emulator for computationally expensive Relativistic Hartree-Bogoliubov and Quasiparticle Random Phase
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, recovery and separation of supernatant, and drying process. Support AI‑enabled data analysis, including collaboration on Artificial Neural Networks and Gaussian Process modelling, to accelerate processing