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other machine learning models. Generate and evaluate hydrologic hindcasts and forecasts to assess model fidelity, forecast reliability, and predictive skill across subseasonal to annual time scales
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experience in manufacturing systems modeling, simulation (i.e., DES), and digital twins. • Good knowledge and experience in machine learning, reinforcement learning, and AI-based optimization for production
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treatments for mental illness. To this end, we bridge computational models that target various levels of analysis, including the algorithms (e.g., reinforcement learning models) and their neural
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these challenges by: Developing predictive workload, lead-time estimation, material planning models to capture the high variability in HMLV environments using hybrid AI (combining machine learning, feature-based
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applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in process industries; advanced process control (APC); model predictive control (MPC); digital
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. This PhD will focus on uncertainty-aware machine learning models, developing and evaluating techniques (e.g., Bayesian and interval neural networks) to quantify model uncertainty and monitor it during
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Learning, or a related field. A Master’s degree is preferred. ASR/TTS Expertise Experience in training and fine-tuning Automatic Speech Recognition (ASR) or Text-to-Speech (TTS) models, preferably in
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on quantitative phenotyping via generative modelling of quantitative MRI data. This exciting PhD position combines advanced machine learning with medical imaging physics to develop next-generation tools
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of physics- informed machine learning and deep learning, with applications to inverse problems in scientific imaging and the modeling of complex physical systems. The overall goal is to integrate the knowledge
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language models from LLMs. Demonstrated publication record in the machine learning and AI field. Excellent programming and computer science skills. Preferred Qualification: Doctoral degree in electrical