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reaction engineering. Proficiency in Aspen Plus or similar process simulation tools. Experience with techno-economic assessments (TEA) of complex chemical or biological systems. Familiarity with CO2 capture
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environmental engineering. A collaborative and analytical mindset, with the ability to synthesize complex information across experimental and theoretical domains. Enthusiasm for advanced electron microscopy
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to represent the Faculty of Natural Sciences in some instances. Your leadership style and personality You are a motivational and unifying leader who understands how to manage a complex organisation with many
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and machine learning, you will help develop new methods for understanding complex failure mechanisms—an area where existing industrial knowledge remains limited. The project will be executed in three
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. Responsibilities and qualifications Qualifications: PhD degree in Engineering, Physics, Computer Science, or Applied Mathematics. Proficiency in scientific programming with Python. Excellent oral and written
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use your skills to answer fundamental biological questions in health and disease. The successful candidate is expected to leverage their skills in proteomics and computational analysis to interrogate
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, artificial intelligence (AI), machine learning, and computation have emerged as powerful digital technologies for creatively generating new design ideas and rapidly advancing formgiving methods within
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of classical computers will require thousands, or even millions, of qubits. This creates a major engineering hurdle: interconnect complexity. Each qubit needs dedicated control and readout lines, which
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variable and climate-sensitive ecosystems, which poses great challenges for their mapping. You will be part of a research project aiming to unravel the complex interactions within peatland ecosystems using
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variable and climate-sensitive ecosystems, which poses great challenges for their mapping. You will be part of a research project aiming to unravel the complex interactions within peatland ecosystems using