300 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "NORTHUMBRIA UNIVERSITY" research jobs in Sweden
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description and working tasks The project will develop privacy-aware machine learning (ML) models. We focus on data-driven models for complex and temporal data, including those built from synthetic sources
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infrastructures organized in infrastructure platforms, of which the Vibrational Spectroscopy Core Facility (ViSp) is a central infrastructure for this project (https://www.umu.se/en/research/infrastructure/visp
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together with Jendrik Seipp, Senior Associate Professor in Artificial Intelligence at LiU. The research projects for the advertised position will be in the areas of automated planning and machine learning
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broad spectrum of fields, from core to applied computer sciences. Its vast scope also benefits our undergraduate and graduate programmes, and we now teach courses in several engineering programmes
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sector, with a documented history of collaboration with forest companies. Knowledge of Deep Learning frameworks applied to forestry. Experience with synthetic data generation. About us The Department of
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electrochemistry Demonstrated ability to work independently Excellent command of the English language, good communication, and team working skills If not already proficient, be willing and able to learn X-ray
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it effect engagement and learning. For more information about the Akelius Math Learning Lab, see: https://www.chalmers.se/institutioner/mv/akelius-math-learning-lab/ Who we are looking
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Join and help us to derive global forest biomass data from the European Space Agency’s Biomass satellite mission. If you have interests in remote sensing, machine learning and forests, this is the
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Chemical Biological Centre (https://www.umu.se/en/kbc ) at Umeå University and is affiliated with the national Centre of Excellence – Umeå Centre for Microbial Research (UCMR) (https://www.umu.se/en/ucmr
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for radio-based positioning and sensing (localization, tracking, ISAC), combining physical modeling, probabilistic inference, and modern machine learning in collaboration with international partners. About us