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spectroscopy, LIBS and/or XRF, together with calibration, multimodal co-registration, data fusion and machine-learning methods.; The research will involve several main tasks:; • Underwater Sensor Development and
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sciences. Its vast scope also benefits our undergraduate and graduate programmes, and we now teach courses in several engineering programmes at bachelor’s and master’s levels, as well as the programmes in
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parental leaves, military service etc.) Excellent/good skills in oral and written English. English is used as the language of instruction and supervision in this position. Expectations: A formal background
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dissemination is expected to focus on leading Human-Computer Interaction venues. For further information about the project, see: https://dff.dk/en/our-funded-projects/meet-the-researchers/research-leaders
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/ESA_selects_Harmony_as_tenth_Earth_Explorer_mission ) The candidate will develop and apply cutting-edge remote sensing or photogrammetry methods, in combination with data science approaches such as machine learning and data assimilation via
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in Machine learning, Natural Language Processing, and/or Software Development. Applicants with an experience in one or more of the above academic fields are encouraged to apply. High proficiency in
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, Machine Learning, or related areas. - Knowledge of Large Language Models and Retrieval-Augmented Generation. - Experience or interest in Knowledge Graphs, Semantic Web technologies, information retrieval
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. / Proven experience in the application of machine learning and deep learning (ML/DL) techniques to image processing. Experiencia demostrable en la implementación de sistemas electrónicos para la
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for a Professorship (W2) (5 years/tenure track) of Statistical and Machine Learning in the Life Sciences, combined with the lead of a research group Computational Statistics & Dynamical Systems for Life
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different dyadic motor coordination tasks. A range of neurophysiological measures (EEG, ECG and fNIRS) as well as behavioural measures will be recorded simultaneously from both partners. Machine-learning