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Field
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Your Job: Work on a wide range of computer vision and machine learning methods and applications focusing on the aspects outlined above, inspired by the needs of societally relevant applications, e.g
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Future. Discover. Together. The Group for Applied Machine Learning at the Department for Artificial Intelligence at Fraunhofer Heinrich Hertz Institute in Berlin is looking for a student assistant
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Temporary Position for 12 Months, starting from 01.04.2024QualificationsWe are looking for somebody with a masters or PhD degree with experience in machine learning / data science.The candidate
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engineering, software design, software engineering, computer engineering, technology management, industrial engineering or comparable. In dosing technology, various components, such as those needed
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19 Mar 2024 Job Information Organisation/Company Forschungszentrum Jülich Research Field All Researcher Profile Recognised Researcher (R2) Established Researcher (R3) Country Germany Application Deadline 14 Apr 2024 - 22:00 (UTC) Type of Contract To be defined Job Status Other Is the job funded...
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in Computer Science or a related field Experience in software development Excellent programming skills Familiar with basics of machine learning Interest in mobility technology and/or simulations, with
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Your Job: Our team is dedicated to creating a generic numerical solution framework for SOCs, utilizing an open-source approach, and implementing the innovative concept of a “digital twin.” This framework aims to provide a comprehensive understanding of the transport phenomena occurring within...
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also be able to work on machine learning method development. About Us Located in the prosperous capital of Bavaria and home to over 40000 students, the Technical University of Munich (TUM) is one
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Machine Learning and Computer Vision for remote sensing and processing satellite data. Our group studies natural and designs artificial cognitive systems, researches their relationships and develops methods
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, and create machine learning tools to integrate bacteriophage and bacterial (meta-) genomics data. Analyse phage-host association datasets, which are often noisy and require specific knowledge