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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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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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. 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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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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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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of machine-learning-infused atomistic modeling techniques and their application to important problems in chemistry, physics and materials science. Together, you will help advance a key scientific
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Caro's group, whose core expertise is the development of machine-learning-infused atomistic modeling techniques and their application to important problems in chemistry, physics and materials science
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. Adding to our strong expertise in traditional atomistic modeling, we have been early adopters of atomistic machine learning and other data-driven techniques that have recently emerged in the field
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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