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with a unique problem- and project-based focus, and continued education and knowledge dissemination. The candidate will be supervised by Kim Guldstrand Larsen, Jiri Srba, and Christian Schilling. Your
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machine learning and signal processing, with preferably some knowledge of RF systems or spectrum sensing. You are confident working with experimental RF data such as baseband I/Q signals, developing and
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strong knowledge in at least two of the following research directions: statistical signal processing, e.g., detection theory and estimation theory; signal compression & sparse representations, e.g
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RF/test & measurement equipment. It is desirable that you have strong knowledge in at least two of the following research directions: RF hardware integration, e.g., mixers, amplifiers, SDR, FPGAs
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level. How to apply Your application must include the following: Application, stating reasons for applying, qualifications in relation to the position, and intentions and visions for the position
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knowledge and technologies. Currently, our department has approximately 230 employees, 70 Ph.D.-students enrolled in the Doctoral School in Medicine, Biomedical Science and Technology, and 1,900 students in
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qualifications at PhD level. How to apply Your application must include the following: Application, stating reasons for applying, qualifications in relation to the position, and intentions and visions
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research environment where focus is on innovation, knowledge sharing/building and interdisciplinary collaborations. The position is within the group on Materials Science and Engineering, which bridges
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international research environment where focus is on innovation, knowledge sharing/building and interdisciplinary collaborations. The position is within the group on Materials Science and Engineering, which
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industrial testing, thereby the candidate should be aware that some experimental validation work is also needed, but a pre-knowledge of the concerned offshore industrial processes is not requested