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/scientific computing and numerical methods for PDEs High‑performance computing (parallel distributed programming, GPU programming) Astrophysical fluid dynamics and/or magnetohydrodynamics Radiative transfer in
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. Lähdesmäki. “Modeling Temporal scRNA-seq Data with Latent Gaussian Process and Optimal Transport”. In: Proceedings of the 43rd International Conference on Machine Learning. OpenReview . 2026. What we offer
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steel roof deck systems exposed to different fire scenarios. The research methods include furnace fire tests, numerical simulations and analyses on the full-scale perforated steel deck system
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understand the structural behaivour of perforated steel roof deck systems exposed to different fire scenarios. The research methods include furnace fire tests, numerical simulations and analyses on the full
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, such as develop and validate and optimize Aspen simulations related to biobased processes with experimental data from lab/pilot-scale systems. Experience in performing LCAs in the forest and lied sectors
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, mathematical methods in engineering physics, and a demonstrated interest in numerical methods, and high-performance computing. Applicants should hold a master’s degree with excellent academic performance in
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demonstrated interest in numerical methods, and high-performance computing. Applicants should hold a master’s degree with excellent academic performance in Mechanical Engineering, Technical Physics, or a closely
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optimization, adiabatic quantum computation, and quantum machine learning. Theoretical and applied research into the capabilities of quantum computing and quantum advantage is a prospective topic across four
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has focused on the theory and applications of quantum walk algorithms, variational quantum algorithms and their optimization, adiabatic quantum computation, and quantum machine learning. Theoretical and