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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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development of machine-learning-infused atomistic modeling techniques beyond the state of the art and their application to study important problems in chemistry, physics and materials science. The group has
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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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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
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intelligence, robotics, machine learning, and human-robot interaction. Project Description The focus of the project is artificial intelligence (AI) and specifically the use of large language models in health
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ADC performance against acute myeloid leukaemia (AML). Laboratory experiments and machine learning models will be implemented to achieve the following aims: Develop a random forest regression model
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effective teaching experience who can teach online (Canvas), on campus, and/or at off campus locations and who can teach introductory and advanced courses in women’s studies, gender, masculinities
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through applied research programmes. Faculty in the ICT Cluster undertake funded industry-relevant research, teach courses in Computer Science, Computer Engineering, Information Security and Software
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optimisation. State-of-the-art digital models and AI tools that incorporate machine learning could enable predictions of the dry fibre forming that are subsequently used as input into the RTM process model
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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