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accumulate in groundwater and are difficult to clean up during drinking water production, posing significant concerns for society. Chemical identification in complex samples is time-consuming, necessitating
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Cadence Virtuoso, Spectre, or Mentor Graphics. Understanding of semiconductor device physics, especially MOSFET operation, modeling, and scaling. Strong analytical and problem-solving skills, with
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sustainable energy. Responsibilities and qualifications Biohybrid systems offer unique energy-efficient routes to harness solar energy for the fixation of CO2 and nitrogen into valuable, complex molecules
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scientists covering a broad range of expertise in photonics and electronics. The Project in Short The project focuses on developing numerical modeling and optimization tools to explore the information
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intelligence. This PhD project will leverage the power of field-programmable gate arrays (FPGA) to deploy machine learning models on the edge with low latency and high energy efficiency. This added intelligence
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of the following areas and an interest to develop within others: Protein chemistry Enzyme kinetics and kinetic modelling Experimental physical chemistry Electrochemistry Assay development and
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designs, building effective and conceptual models to inform our theoretical understanding, and developing code and theory frameworks to address new topological phenomena. Depending on the project’s results
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restraint conditions. A key goal is to develop both a sensor system and a prediction model for the short- and long-term deformation behaviour of concrete. These tools will be applied to full-scale structural
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uses of prescription tranquilizers’ headed by Associate Professor Margit Anne Petersen and funded by Independent Research Fund Denmark. The overall research project focuses on the complex relationship
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collaboration within e.g. nutrition, chemistry, toxicology, microbiology, epidemiology, modelling, and technology. This is achieved through a strong academic environment of international top class with