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or an equivalent qualification, plus at least ten years of project management experience in conducting interior and furniture design. Applicants for the Postdoctoral Fellow post should have a doctoral
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The School of Mechanical and Aerospace Engineering (MAE) is seeking to hire a Research Fellow to work on the design, manufacturing, team building, and industrial promotion of an electrical
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Science, Statistics, or other related fields. Entry Level candidates are welcome to apply At least 2 years of relevant experiences in IC design/Electronics Familiar with Analog/Power Management/RF/ADC/DAC IC design
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. • Demonstrated expertise in recombinant protein expression, purification, and biochemical characterization. • Experience in one or more of the following areas: computational protein design, protein engineering
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industrial background. The key is having good hands-on experience in sustainable design and implementation, or the enthusiasm to pick up new skills. The postholder will have the full support of a large inter
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scientific publication. You will be able to plan and execute experiments to a high standard, with a creative approach to problem-solving. You will be collaborative, direct about what you know and don't know
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to the electrification applications including electrical motor drives for transportation systems (land and sea) Thermal/cooling management, integrated (inverter-motor) system design and development, motor (vernier and
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mechanics, solid-state physics/chemistry, and/or quantum mechanics • Practical experience with density functional theory calculations (e.g., VASP, Quantum Espresso, Q-Chem, PySCF) • Proficiency in
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. • Plan experiments, collect and analyse data using appropriate analytical tools and software. • Work with interdisciplinary teams including clinicians to integrate microfluidics chips with complementary
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prototypes and interaction concepts through iterative user-centered design cycles with end-users. Run benchmarking experiments and user studies with human operators, including experimental setup, data