80 engineering-computation "https:" "https:" "https:" "https:" "https:" "https:" "https:" scholarships in United Kingdom
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application for this project at http://www.bris.ac.uk/pg-howtoapply . Please select
on the Programme Choice page. You will be prompted to enter details -
-engineering@kcl.ac.uk Department of Informatics : informatics-pgr@kcl.ac.uk Department of Mathematics : mathematics-pgr@kcl.ac.uk Department of Physics : physics-pgr@kcl.ac.uk Where to apply Website https
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publications and conferences. Qualifications: Completed or nearing completion of a Master's degree in Medicinal Chemistry, Chemical Engineering, or a related field. A background in organic chemistry, and/or high
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Department/Location: Department of Biochemistry, Central Cambridge PhD Position - Marie Curie network ON-Tract: Protein engineering of enzymes: in vitro directed evolution and machine learning-based
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(2.1 or above or international equivalent) and/or Masters degree in a relevant subject (physics, mathematics, engineering, computer science, or related subject) Proficiency in English (both oral and
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outlining how you would approach the project, and an up-to-date CV. Under programme name, please select 'Architecture, Building and Civil Engineering'. Please quote reference RAINDROP-CH. Only applicants with
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second-class honours degree (or equivalent) in neuroscience, computer science, engineering, bioinformatics or related fields. Strong Python and/or R skills and clear communication are essential; experience
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of novel payloads and to engineering monoclonal antibodies. Yet one critical component (the linker) remains comparatively under-explored. Linkers govern the controlled release of potent payloads inside
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to work with Florian Hollfelder at the Biochemistry Department of Cambridge University (https://hollfelder.bioc.cam.ac.uk/ ). The project is part of the Horizon Europe Eu Marie Curie Network MetaExplore
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Location: Central Cambridge PhD Studentship - Marie Curie network ON-Tract: Protein engineering of enzymes: in vitro directed evolution and machine learning-based elaboration of biocatalysis