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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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phenomena and development of biobased barriers, foams, and emulsions Advanced textile recycling, including flotation‑based methods Learn more: https://www.aalto.fi/en/department-of-bioproducts-and
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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
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of Electrical Engineering. Thus, please see the student information and admission criteria at https://www.aalto.fi/en/study-options/aalto-doctoral-programme-in-electrical-engineering . Desired
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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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: Extending full-spectrum non-gray models for gases to solid particles Experimental characterization of fuel particles using FTIR Mathematical model formulation and programming Model implementation in
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conduct research on the topic. Have programming skills (e.g., Python) and experience with numerical simulations or quantum software frameworks (e.g., Qiskit and PennyLane). Have a demonstrated ability to
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, computer science, electrical engineering, or a related field. Strong interest in doctoral research on audio deepfake detection, watermarking, source tracing, and generative AI. Good programming skills
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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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programming, GPU programming) Astrophysical fluid dynamics and/or magnetohydrodynamics Radiative transfer in astrophysics Machine learning or data analysis for physical sciences Strong programming