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negotiable within reasonable limits. The project investigates the role of common ground in linguistic communication from the point of view of language evolution. The notion of common ground plays a central
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related field. You bring a strong analytical background and are proficient in areas like geometric deep learning, signal processing, statistics, or learning theory. Knowledge of energy systems, multi-energy
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Innovation. This position brings together four research groups at TUM: the Chair of Data Science in Earth Observation (Prof. Xiao Xiang Zhu), the Chair of Hydrology and River Basin Management (Prof. Markus
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that target diverse biological processes. These include DNA replication, transcription, translation and cell signaling. To exemplify, nucleoside analogues possess an accomplished history within therapeutic
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one pdf file). Please indicate the job announcement ID (3163). Review of applications will begin on November 1, 2025, and continue until the position is filled. If you have any questions, please contact
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qualifications for the position, 1 recommendation letter, and a CV (Europass type) including information on university degrees (if possible with Grade Point average scores) and optional additional reference
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surgery. This PhD position will focus on the health monitoring system development mainly based on multimodal physiological signals, for instance, inertial measurement unit (IMU), electrocardiography (ECG
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1 Research Fellowship (Master) - 101112877 - UPSTREAM - HORIZON-MISS-2022-OCEAN-01-04 - #NOVAID-B396
phase may be replaced by a declaration of honor as stated in point no. 2). * For academic degrees conferred by a foreign higher education institution, it is mandatory to present the record of recognition
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excellent analytical skills and is highly motivated and rigorous. Has good technical understanding of (or ability to learn) research design and methodologies as well as advanced signal processing and
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student will become part of a team at DTU with expertise in digital signal processing methods, and machine learning methods for amplitude and phase noise characterization of optical frequency combs