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If you experience technical problems, please contact hcm-support@sdu.dk. Application procedure Applicants are advised to read the SDU information on how to apply . Assessment of the candidates is based
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. The research will bridge both established and emerging technical expertise within the section, encompassing areas such as FPGA and neuromorphic computing, Edge AI, machine learning, power electronics, and self
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considerations. Experience working with machine learning methods for control, perception, or decision-making in physical systems is an advantage. Knowledge of or a passion for sustainable computing
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on the implementation of the European Commission’s Safe and Sustainable by Design (SSbD) framework. The position is based in Odense, Denmark, and part of the CEB JU-funded ALLIANCE project. The position is available
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: Develop algorithms for translating the EEG recorded signals for seizure detection and prediction. Implementing the developed algorithms on FPGA-based SNN and/or ASIC designed SNN chip with focus on accuracy
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contribute to the teaching activities of the section of Mechanical Engineering by teaching 1-2 courses per semester. You will focus on developing and extending in-house computational codes based on open-source
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. The project is based on the novel application of formalisms, algorithms, and computational methods from computer science to the modeling of microbial communities, for use in health science. We aim to expand
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learning methods. Familiarity with or interest in behavioral software systems – for example, development of applications or tools that incorporate nudging, persuasive technology, or behavior change
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programming and ease with formal thinking are prerequisites. The project is based on the novel application of formalisms, algorithms, and computational methods from computer science to the modeling of microbial
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deadline is August 14, 2025, at 11.59 PM/23.59 (CET/CEST Assessment of the candidates is based on the application material, and an application must include: A motivation letter that clearly explains