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Measurements and Data Processing as per October 1, 2025, or as soon as possible thereafter. The position is available for a period of 1 year, with the possibility of extension. In electronic engineering, Aalborg
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characterization in complex in-situ environments. The key responsibility of the position is to develop post-processing methods to extra essential features from the collected measurement data despite drone positional
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, technology, and sustainability. About research We are looking for an innovative researcher with a background in music cognition, psychology, music technology, or musical data science, ideally with a
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mechanistic information on human brain changes in pain conditions. Your research will help us understand the mechanisms involved when pain becomes chronic. Work in a team using electroencephalography (EEG
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challenging environments, where signals are extremely noisy and distorted, and include severe linguistic variations, particularly when data and computational resources are scarce. This will be tackled using a
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whether they have been shortlisted for assessment or not. When the hiring process is completed, a final rejection will be sent to the applicants who are not considered for the position. Contact information
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, fresh air and easy access to natural parks, forests, and great beaches. Information about life in Denmark in general, health care, tax etc. can be found at the International Staff Unit page https
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to the position. References/recommendations. Personal data. The applications are only to be submitted online by using the"Apply online" button below. Assessment The assessment of candidates for the position will be
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relating to thermal stress responses. Additionally, the postdoc will also be developing methods for integrating thermal tolerance measures with microclimate data from field sites and climate models to better
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and neurophysiology(EMG/EEG). You have extensive data processing experience(Python, Matlab or other platforms), being able to independently conduct complex signal processing and machine learning