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in a relevant engineering field, experience in programming and interest in applying AI tools in electromagnetic modeling. In particular, experience with or interest in some of the following are
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. Thus, please see the student information and admission criteria at https://www.aalto.fi/en/study-options/aalto-doctoral-programme-in-electrical-engineering . Desired background We are looking for
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calibration routines for photoacoustic and optical aerosol measurements. The work combines hands-on instrumentation, laboratory experiments, uncertainty analysis, programming and scientific data analysis
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-resolved measurements of their bidirectional reflectance distribution function (BRDF). The work combines optics, precision measurement, surface characterization, programming and quantitative data
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developing, testing and validating new traceable calibration routines for photoacoustic and optical aerosol measurements. The work may also include experimental design, uncertainty analysis, programming
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. A strong background in energy-system modelling, mathematical optimization, machine learning, reinforcement learning, or data-driven decision-making is appreciated. Experience with programming and
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graph theory. Computational methods are central; the team operates a 500‑core cluster. We both compete with and leverage AI; see: https://www.aalto.fi/en/news/researcher-cracks-new-kissing-number-bounds
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algorithms and data analytics tools, and share your results with experimental collaborators. The position is part of the Research Council of Finland project ELPH-ML ( https://research.fi/en/results
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and admission criteria at https://www.aalto.fi/en/study-options/aalto-doctoral-programme-in-electrical-engineering . Desired background We are looking for candidates with a strong background in one or
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research experience in mechanics Prior exposure to numerical/computational modelling methods (e.g. discrete element method or finite element analysis) Strong programming skills Good written and verbal