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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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candidate should have strong programming skills, particularly in Python, and experience with machine learning algorithms suited for cloud-edge, mobile, or IoT environments. Experience in prototyping and
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with bioprocess modelling and simulation tools (e.g., Aspen Plus, MATLAB, or Python), TEA, and LCA. Strong data analysis, problem-solving, and teamwork skills. Commitment to sustainability and research
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Mechanical, Materials, or Biomedical Engineering, or a related field. Interest in biomaterials, mechanobiology, or modelling. Familiarity with computational tools (Matlab, Python, or finite element analysis
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requirements: Excellent analytical skills Strong verbal and written communication skills Desirable: Strong proficiency in optimising and modelling power and energy systems Programming skills in Python and Julia
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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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models, in-house laboratory tests in a wind-wave-current flume (https://research.ncl.ac.uk/amh/ ) and numerical methodology to quantify biofouling impacts on flow-induced vibration phenomena, structural
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their operational reliability. The PhD student will combine mathematical models, in-house laboratory tests in a wind-wave-current flume (https://research.ncl.ac.uk/amh/ ) and numerical methodology to quantify