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, systems engineering, geometry assurance, automation, or model-based development. The project addresses how advanced digital infrastructure can be used to reduce uncertainty, risk and variation in
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) study. This large-scale, longitudinal project is designed to advance computational psychiatry by integrating mechanistically informed behavioral tasks with theory-driven mathematical modeling in a
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machine learning, reduced-order modeling, or data-driven modeling of physical systems. You will conduct research on the development of fast, trustworthy, and data-efficient surrogate models that
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experience and capability in handling quantitative data and programming. If you already have used data-driven computational methods to model cognitive or behavioural change in any substantive domain, that
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datasets and labeling processes. Machine Learning & Model Development Design, develop, optimize, and support machine learning and deep learning models. Prepare datasets for training, validation, and testing
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Proficiency in GUI development (e.g. using Python Dash , Tkinter , or web-based frameworks ) Experience working with data-driven simulation and model validation in power grids (Ph.D level is mandatory
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well as practical experience in dynamic model development and the analysis of large-scale biological data, who are excited to apply rigorous computational modelling approaches to real biological and
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governs their thermal performance, operational robustness and cost. The overall aim of the project is to design, optimize and digitally model the cooling system of a pilot-scale electrochemical
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(NUS), and BEARS. The role will focus on developing and implementing AI/ML-driven high-throughput, closed-loop experimentation for the discovery and optimization of electrocatalytic materials for CO
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) environments, GPU-enabled systems, and cloud-based resources for large-scale data analysis and model development. Using these rich data resources, we develop and apply advanced AI/ML methods to model tumor