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for latent variables and their connections to modern machine learning. The project combines methodological research in statistics with applications to large-scale social science data. The successful candidates
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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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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models
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scientific-programming skills, practical experience of computational model development, and an interest in applying machine-learning or data-driven methods to physical systems. Experience of QTFs, BEM software
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of interest supporting the instructional needs in our major, minor, and university-wide electives. Specific areas of need are spreadsheet modeling and analysis, machine learning, and project-based capstone
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Join the Responsible Machine Learning (ML) Group at the Faculty of Computer Science. Led by Prof. Dr. Martin Pawelczyk, who recently joined the University of Vienna from Harvard University, our research
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modelling, automated neuroanatomical phenotyping, machine learning and advanced statistical approaches. The doctoral project will be developed jointly with the successful candidate and tailored
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. Duties/Responsibilities: 1. Data Management, Review, & Analysis (70%) Advanced Neural Signal Processing: Apply quantitative analysis, signal processing, and machine learning methods to high-dimensional EEG
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structure modeling in cancer immunotherapy design. Profile A — AI PhD in machine learning, computer science, computational science, or a related field. Strong experience with deep learning (e.g., PyTorch
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computation, probabilistic machine learning, latent-variable models, unsupervised learning, or matrix and tensor factorization is an advantage. Experience with computational methods for large or high