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Skills Background in biochemical or chemical engineering, biotechnology, or microbial processes. Hands-on experience in fermentation, microbial cultivation, and analytical techniques (HPLC/GC). Familiarity
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bone cells toward regeneration. These will be characterised using AFM and rheology, followed by immune–osteogenic co-culture studies to evaluate mechanotransductive signalling. The resulting data will
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, including but not limited to computer science, data science, engineering or mathematics, who are passionate about machine learning and AI research. Strong analytical thinking, problem-solving skills, and the
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-culture studies to evaluate mechanotransductive signalling. The resulting data will inform mathematical models linking material mechanics to biological responses, enabling the sustainable design of next
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. Hands-on experience in fermentation, microbial cultivation, and analytical techniques (HPLC/GC). Familiarity with bioprocess modelling and simulation tools (e.g., Aspen Plus, MATLAB, or Python), TEA, and
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culture, biomaterials, or biological assay techniques. Good analytical and problem-solving skills and the ability to work both independently and in a multidisciplinary team. Excellent written and verbal
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machine learning and AI research. Strong analytical thinking, problem-solving skills, and the ability to engage with complex data challenges will be greatly valued. Experience with Python or AI frameworks
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. Moreover, a 12-week placement and regular meetings with Syngenta will give the student first-hand experience of research in a world-leading industrial setting. For more information about research in
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subject or First or upper second class UG degree (2:1). Enthusiasm for research, the ability to think and work independently, excellent analytical skills and strong verbal and written communication skills
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independently, excellent analytical skills and strong verbal and written communication skills are also essential requirements. This studentship is open to UK/Home applicants only, candidates must meet one