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-constrained devices such as wearables, smart sensors, hearables, and IoT nodes. While current deployment methodologies can optimize models before deployment, the resulting software remains static throughout
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hardware-software co-design of ML models taking inspiration from the brain. The applicant should: have a Master’s degree in Engineering with a background in Electrical Engineering, Computer
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of materials or you have a profound interest in these topics. Ideally you have experience with either COMSOL, ANSYS or another CAE simulation software. You are highly precise and detail-oriented, consistently
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-ILC consortium and will contribute reusable software, model documentation and scientific publications. We are looking for a motivated and curious researcher with a strong computational background and an
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this additional qualification.-You have strong skills in statistical, econometric and quantitative data analysis and have experience with statistical software packages; furthermore, you are keen to develop
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stability assessment of converter-dominated transmission systems. The research combines power systems, numerical methods and scientific computing, with a strong emphasis on software development and practical
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. • Education: You do not hold a doctoral degree. You hold a Master’s degree in software engineering, ICT, computer science, game development, or related fields obtained with at least distinction (cum laude
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of the Division of Agricultural, Food and Resource Economics. You have experience with or are eager to work with large databases and database management software. You have starter skills in statistical, econometric
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major impact on the way combinatorial optimization software is developed, evaluated, and used: the proofs produced will enable (1) debugging, since proofs contain detailed information about where