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accurate, well-characterized methods that serve as traceable standards for biomarker quantification, enabling reliable and reproducible measurements across different assays. In this PhD project, you will
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exposed to Bayesian optimization to find the optimal set of parameters that improve process performance and material quality. Secondly, different machine learning strategies based on traditional supervised
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. This is because experimental techniques to solve structures of protein complexes favor more stable interactions with larger interfaces and because we lack efficient algorithms to compute similarity between
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optimizations tailored to different environments. The optimizations range from algebraic optimizations (e.g., term rewriting) to algorithmic optimizations (e.g., group level algorithms), and to hardware
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security vulnerabilities. You will innovate the Find2Fix pipeline by making the different steps, including found issues and suggested patches, easier to understand using interpretable AI using state machine
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Learning Centre; A complete educational program for PhD students; Multiple courses on topics such as time management, handling stress and an online learning platform with 100+ different courses; 7 weeks
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tool that allows developers to quickly find and fix software errors including security vulnerabilities. You will innovate the Find2Fix pipeline by making the different steps, including found issues and
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of parameters that improve process performance and material quality. Secondly, different machine learning strategies based on traditional supervised learning techniques (e.g. random forest (RF
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local heat solutions for PEDs. The EmPowerED project involves in total 11 PhD positions at several universities, focusing on different aspects of positive energy districts. The project also involves a
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work will focus on identifying the mathematical knowledge and properties to guide hardware optimizations tailored to different environments. The optimizations range from algebraic optimizations (e.g