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oppdatert 24.03.2026 Hva er en cookie? En cookie er en liten datafil som lagres på datamaskinen, nettbrettet eller mobiltelefonen din. En cookie er ikke et program som kan inneholde skadelige programmer eller
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knowledge of power system security and machine learning being crucial. The Associate will primarily work alongside National Grid engineers to integrate the machine learning backend of the intrusion detection
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the interface of machine learning and biology (tools developed by the team: https://github.com/cantinilab). The team is composed of 8 people : 3PhD students, 3 post doc, 1 research engineer and 1 assistant
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Photonicsens SL in Valencia, Spain, and one 3-month at Polytechnic University of Valencia are foreseen in the research plan. Opportunity to pursue a PhD degree at a leading European university within a
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, the Optimized AI team focuses on computing techniques to scale up a wide range of AI approaches and drive the SW-HW-Technology co-optimization. We analyze and implement AI models and software architectures while
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visible research, strong industry links and diverse environment create a collaborative setting where ideas grow into real impact. At the division of Interaction Design and Software Engineering , we design
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data for European and international funding applications. Specific Requirements PhD in Physics, Engineering, Mathematics, Computing, Bioinformatics or related fields. Master's degree in Data Science
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systems, ideally including mobile manipulators. You will be proficient in software development (Java, Python, C++), with object-oriented programming and software engineering practices. You will have hands
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teaching at the secondary or postsecondary level or in industrial training programs. Desirable Education and/or Experience • PhD in electrical or computer engineering or a closely related field, from
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vision-based platforms. You will work at Forschungszentrum Jülich, in close collaboration with bioimage analysis partners at Karlsruhe Institute of Technology. Your tasks in detail: Develop and extend deep