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Department: School of Computer Science Details of Studentship: Fully Funded PhD Studentships Applications are invited from Home and International students for a number of fully-funded PhD
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-edge advancements in automated drug discovery and bio-instructive material manufacture. The project aims to utilise flower waste as a sustainable feedstock to discover new bioactive small molecules, then
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research group (CfAM) at the University of Nottingham. The student will work in world-class laboratory facilities in the CfAM engaging with interdisciplinary team with expertise in 3D printing, bio-printing
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manufacturing (CfAM) and advanced bio-printing (Additive BioFabrication Laboratory), and will provide experience with new and advanced 3D-printing equipment not available elsewhere. This project is aligned with
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manufacturing (CfAM) and advanced bio-printing (Additive BioFabrication Laboratory), and will provide experience with new and advanced 3D-printing equipment not available elsewhere. This project is aligned with
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electronically-active materials, including 0D and 2D nanomaterials. Advantages: This studentship will take place in world-leading research laboratories for additive manufacturing (CfAM) and advanced bio-printing
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-material additive manufacturing. This studentship will take place in world-leading research laboratories for additive manufacturing (CfAM) and advanced bio-printing (Additive BioFabrication Laboratory) and
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. What you should have: A 1st degree in physics or engineering. An interest in optics, some ability in computer programming A desire to learn new skills in complementary disciplines. You will work jointly
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for fusion components. This framework foresees two building blocks: high-fidelity Computational Fluid Dynamics (CFD) simulations of boiling flows within complex geometry using opensource software and cutting
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necessarily require formal education in geotechnics. Applicants with a background in mechanical/materials engineering or alternatively mathematics/computer science with an interest in numerical modelling