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for manufacturing operations. Process control: process modelling, control, and optimization, with applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in
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, electrical & electronic engineering, or equivalent. Background knowledge in signal representation/processing, visual data compression, and data-driven and machine learning/analysis. Prior research experience
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-driven organization where we are committed to student success and positively transforming the community through scholarship and service. We thrive on innovation, making an impact, and fostering
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PhD in a relevant field (Computer Science, Mathematics are most likely to fit the role, but we are open to Chemistry, Materials Science, Chemical Engineering, etc.), expertise in cutting-edge AI and
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PhD in a relevant field (Computer Science, Mathematics are most likely to fit the role, but we are open to Chemistry, Materials Science, Chemical Engineering, etc.), expertise in cutting-edge AI and
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to create new designs that optimize water yield. The project aspires to elucidate the physics governing droplet impact and wetting on fibrous networks in order to enhance fog net technology. The planned work
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an emphasis on technology, data science and the humanities. We are looking for a Research Fellow to conduct AI for medicine research. The role will focus on developing foundation models to medical image
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on fibrous networks in order to enhance fog net technology. The planned work is experimental and will be conducted in our lab facilities, also incorporating theoretical models of complex flow. Fieldwork is
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frameworks, such as PyTorch (preferred) or TensorFlow, with a preference for experience implementing SOTA models and training procedures from academic journal papers. Development of data engineering pipelines
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Temporary/Per diem Organization Type: Higher Education Institution Required Education: Doctorate/Professional Internal Number: JR102485 Job Description Summary The Department of Engineering Technology at