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Field
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, with expertise in dynamic/data-driven modeling, optimization, Aspen, COMSOL, or CFD b. Techno-economic analysis (TEA) & life-cycle assessment (LCA) for chemical, energy, or biomanufacturing processes
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-functional properties such as antioxidant activity Identify optimal processing conditions that minimise bioactive losses for functional beverage applications. Design and formulate insect protein-based oil-in
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)Part-timeResearch Fellow position (Special Scientist for Research, Researcher A – R3*) at the Department of of Civil Engineering and Geomatics in the field of “Atmospheric Physics using active and
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-polysaccharide interactions under different food processing conditions. Profile phenolic compound and evaluate the bio-functional properties such as antioxidant activity Identify optimal processing conditions that
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social and critical infrastructure in vulnerable communities. About the role The role will run initially for two years, extendable to three. The successful candidate will be able to support and contribute
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Research Fellow to join our multidisciplinary team aiming to develop the next generation of metal multimaterial additive manufacturing. The successful candidate will work on the development of a novel laser
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work on advanced photonic packaging and silicon photonics. The project focuses on developing low-loss optical coupling and packaging solutions for photonic integrated circuits, including chip-to-fibre
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Harvard Office of Technology Development (OTD): Business Development Fellowship Eligibility: Harvard graduate students and postdocs Deadline Month: May Description: This year-long program provides
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Inverse Methods and Ionospheric Modelling Research Fellow - School of Engineering - 106995 - Grade 7
to £46,049 with potential progression once in post to £48,822 Grade: 7 Full Time, Fixed Term contract up to 3 years Closing date: 15th July 2026 UK and International Travel may be required for this role
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of efficient AI-for-Science systems, leveraging foundation models (including small language models) and parameter-efficient adaptation techniques such as LoRA, grounded in a strong understanding of optimization