262 machine-learning "https:" "https:" "https:" "https:" "The Open University" Postdoctoral research jobs in Sweden
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Description of the workplace The position will be placed at the division of Computer Vision and Machine Learning at the Centre for Mathematical Sciences. The Centre for Mathematical Sciences is an
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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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metaproteomics approaches Analyzing large-scale multi-omics and clinical datasets to investigate individual metabolic responses to diet. The work includes applying advanced statistical and machine learning methods
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Navigation and Perception Lab (https://rnp.aass.oru.se ) which belongs to the Centre for Applied Autonomous Sensor Systems (AASS) research environment (https://www.oru.se/english/research/research-environments
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authority. Learn more about our benefits and what it's like to work and grow at KTH. Trade union representatives Contact information to trade union representatives. To apply for the position Log into KTH's
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, pathology, policy and planning. We teach bachelor, Masters and PhD level courses addressing all of these subject areas. For more information about the department or division visit: Southern Swedish Forest
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, and demonstrated ability to develop computational pipelines for biological datasets. Experience in statistical modeling and/or machine learning applied to biological systems, with the ability to link
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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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of working with motion capture, eye tracking, machine learning, or other advanced behavioral analyses or related research experiences. A consistently excellent academic track record is required, including
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multi-modal perception and machine learning. Current noninvasive agricultural monitoring systems rely primarily on passive sensing, which limits sensitivity to early-stage plant stress. This project