21 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" PhD positions at Linköping University
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of machine learning which clearly integrates the two subject areas within the division. For more information about STIMA, please see https://liu.se/organisation/liu/ida/stima . Linköping University is
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application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is inherently spatio-temporal, i.e. physical processes
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meritorious include: Knowledge of machine learning, reinforcement learning, and optimization, Experience with multi‑modal sensor data (vision, force/torque, proprioception), Experience with simulation
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of full-time. Your qualifications You have graduated at Master’s level in biomedical engineering, electrical engineering, machine learning, statistics, computer science, or a related area considered
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world. We look forward to receiving your application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is
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different backgrounds. This position requires that you have graduated at Master’s level in in computer science, media technology, computer engineering, human-computer interaction, visual learning and
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ecosystem applications within AgTech (https://agtechsweden.com/ ), search-and-rescue operations in challenging terrain, and intelligent surveillance for societal security. By combining machine learning with
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(Sensor Informatics and Decision-Making for the Digital Transformation). Read more about the Competence Center here: https://liu.se/forskning/seddit . The focus of this specific PhD project is to explore
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application! We are looking for a PhD student in automatic control at the Department for Electrical Engineering (ISY). Your work assignments This PhD position is part of the ELLIIT research program (https
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series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related areas, but application to dynamic systems is