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Field
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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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Relations in Welfare Professions, Health Promotion in Chronic Ailments, E-learning, Health Technology and Simulation, Life Phenomena and Caring, and PARTAKE: Community Participation. The research centre SHARE
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Berlin conducts interdisciplinary science to understand, anticipate, and shape major disruptions from Artificial Intelligence (AI) to the way we think, learn, work, play, cooperate, and govern. Our goal is
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plan will be prepared that specifies the competencies that the Research Fellow will acquire. Access to career guidance will be provided throughout the doctoral education. Research topic This PhD Fellow
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required, the project involves the analysis of experimental data. The post-doctoral appointee may have the opportunity to teach one or two ten-week courses, contingent on course availability in
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of metabolism, membrane transport, cellular regulation, structural systems biology, machine learning and disease mechanisms. These positions are embedded in the transition from CeMM in Vienna to the newly founded
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, as well as a number of combined degrees with other disciplines. Our research and teaching staff are world leading and world building as they advance knowledge and learning. For more information on our
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UiO/Anders Lien 1st August 2026 Languages English English English PhD Research Fellow in Multisensory Networked Interactions with Robotics Avatars Apply for this job See advertisement About the
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investigate how emerging fuels, such as e-fuels, advanced biofuels, hydrogen carriers, and ammonia-based blends, behave under realistic engine-relevant conditions. Using a specially modified research engine and
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 2 months ago
, Australia. The position is offered in connection with the ARC Discovery Project grant “Responsible Statistical Learning: Uncertainty, Fairness and Transparency”. The successful candidate will play a key role