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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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talented community; a community doing extraordinary things. But what makes us different isn’t only what we do, it’s how we do it. Together, we are driven to be thoughtful, practical, and purposeful in all we
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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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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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-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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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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quantify high-throughput binding data. Examples of suitable backgrounds: Optical engineering, hardware-software integration, image analysis. Building quantitative models: Using high-throughput binding data
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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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Faculty or Department: Faculty of Engineering and Applied Sciences Based at: Cranfield Campus, Cranfield, Bedfordshire Hours of work: 37 hours per week, normally worked Monday to Friday. Flexible