74 data-"https:"-"https:"-"https:"-"https:"-"UNIV"-"L2CM" positions at University of Lund in Sweden
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are united in our efforts to understand, explain and improve our world and the human condition. Description of the workplace The position will be placed in the Division for Computer Vision and Machine Learning
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and Information Technology works broadly with research within cryptography, computer security, wireless and fixed networks. The division runs several research projects through different research
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Doctoral student in Applied Mathematics with a focus on Computer Vision and Spatial AI (PA2026/1165)
are united in our efforts to understand, explain and improve our world and the human condition. Description of the workplace The position will be placed at the Division of Computer Vision and Machine Learning
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environment? Then join us and take the opportunity to make a real difference! For further information, please visit: https://www.lunduniversity.lu.se/about-lund-university/work-lund-university www.sweden.se
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for Neural Rendering for Computer Graphics and Real-Time Rendering. By using ANNs, coded for high-performance on cross-vendor GPUs, we aim to create new techniques for global illumination and material models
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are united in our efforts to understand, explain and improve our world and the human condition. Lund University announces a position of Associate Senior Lecturer in Statistics with a focus on Data Science. The
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conduct research in several areas: analysis of high-dimensional data, Bayesian methods, spatial-temporal models, non-Gaussian modeling, applied research in social science, as well as stochastic models and
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research area MERGE (https://www.merge.lu.se ), focused on climate modelling. Aerosol research has been conducted at Lund since the 1970s and is now a designated profile area at LTH (https://www.lth.se
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includes both scientific and technical staff with expertise in experimental neuroscience, behavioral studies, and data analysis. The position is linked to a research project investigating how humans acquire
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several cancer research groups represented, including joint seminars and other collaborative activities. The group uses various data sources and modern techniques to improve predictive modelling, including