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doctoral researcher(PhD student) with a strong background in probabilistic machine learning, statistics, applied mathematics, computer science, or a related field, and strong programming skills, to work
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mixing. Publishing research in leading journals and conferences in speech, audio, and machine learning, and contributing to open-source releases of software, trained models, and reproducible research
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watermarks. Publishing research in leading journals and conferences in speech, audio, and machine learning, and contributing to open-source releases of software, trained models, and reproducible research
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actions, anticipate future behavior, and reason under uncertainty. The methodological scope includes machine learning, computer vision, multimodal perception, probabilistic modeling, and interpretable
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activity using fully compressible MHD in global and local frameworks, integrated with physics‑informed machine learning and coronal/wind modelling. Key tasks and responsibilities: Develop and implement data
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Karttunen, and Prof. Kari Laasonen, who will supervise the postdoc positions advertised here. We are seeking candidates with expertise in one or more of these areas: Machine-learning interatomic potentials
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implementation in Fire Dynamics Simulator CFD code Experimental validation of simulations Machine learning for accelerated evaluation of radiative properties The doctoral degree will be awarded upon successful
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Experimental validation of simulations Machine learning for accelerated evaluation of radiative properties The doctoral degree will be awarded upon successful completion of advanced doctoral coursework (30 ECTS
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optimization, machine learning, reinforcement learning, or data-driven decision-making is appreciated. Experience with programming and computational modelling is important for the position. Familiarity with
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and data acquisition Direct and indirect sensing methods for bridges, including remote sensing techniques Machine learning and artificial intelligence for infrastructure condition assessment Damage