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EU MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of
between process parameters and material properties will be developed and subsequently exposed to Bayesian optimization to find the optimal set of parameters that improve process performance and material
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part of a team Understanding of dynamical systems, time series models, machine learning, Bayesian statistics, experience in handling environmental and climate data is a merit We offer: This position is
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from any nationality and hold, or expect to hold, a Master’s degree in a related subject. You have a proved track record of academic and research excellence and are fluent in written and spoken English
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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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software (e.g., LLM agents for finding and fixing bugs) Static and dynamic program analysis (e.g., to infer specifications) Test input generation (e.g., to compare the behavior of old and new code via
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CISPA Helmholtz Center for Information Security | Stuttgart, Baden W rttemberg | Germany | 14 days ago
., to infer specifications) Test input generation (e.g., to compare the behavior of old and new code via differential testing) We focus on techniques that apply to real-world software systems. E.g., in the past
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and as part of a team Understanding of dynamical systems, time series models, machine learning, Bayesian statistics, experience in handling environmental and climate data is a merit We offer: This
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, issue tracking). Strong interest in academic research and willingness to pursue a PhD. Independent, structured way of working, quick comprehension, and ability to adapt to new concepts. Excellent
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strong interest in research. A proven track record of scientific work, such as prior publications, is beneficial. We particularly value a solid theoretical foundation in 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴
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. Gaussian Process Regression) model to describe the relationship between process parameters and material properties will be developed and subsequently exposed to Bayesian optimization to find the optimal set