11 machine-learning-postdoc "https:" Postdoctoral positions at Chalmers University of Technology
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and automated floor-plan recognition, to fill data gaps and harmonise information from disparate sources. Learn more and watch our project video here: https://sb.chalmers.se/digital-material-inventories
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Are you interested in developing machine learning algorithms that provably help us make better decisions? Join us as a post-doc in the Division of Data Science and AI, Department of Computer Science
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with modern machine learning and AI technologies to effectively address large-scale problems. About the research project We are seeking a highly motivated Postdoc to join our group in developing
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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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. The position bridges machine learning and molecular science, with opportunities for collaboration, mentorship, and impactful research. About us The Department of Computer Science and Engineering (CSE
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–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience collaborating in interdisciplinary research teams A doctoral
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the last three years prior to the application deadline. Experience in some of the following areas is meritorious: AI and machine learning; convex analysis; functional analysis; mathematical statistics
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will (micro-)benchmark Java-based applications using JMH. You will collect performance measurements from real projects, statistically analyse them, and conduct experiments with modern machine learning
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and machine learning, we collaborate globally to monitor environmental change and support a sustainable future. About the research project The postdoc will work at Chalmers University of Technology in a
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Experience in machine learning Knowledge of SDN and NFV Knowledge of basic TCP/IP protocols What you will do Conduct high-impact research and publish in leading journals and conferences Shape research