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advanced retrieval techniques, including spatio-temporal regularization, and hybrid methods with machine learning and deep learning. He/she will support the development of an improved forest RTM that can
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deep learning. You will support the development of an improved forest RTM that can exploit LiDAR full-waveform data along with hyperspectral signatures. You will plan and carry out field campaigns in
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17 Apr 2026 Job Information Organisation/Company Luxembourg Institute of Science and Technology Research Field Computer science Researcher Profile Recognised Researcher (R2) Positions Postdoc
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DC-26094– POSTDOC/DATA SCIENTIST – AI-DRIVEN CLIMATE RISK MODELLING AND EARLY WARNING SYSTEMS FOR...
applicant will contribute to the AIGLE project by: · Developing innovative scientific Deep Learning/Machine Learning algorithms for flash flood forecasting. · Contributing to the collection
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/admittance, force control Experience with Artificial Intelligence and deep learning concepts for robotics computer vision, tactile sensing, reinforcement learning Experience with robotic simulation tools e.g
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learning, computational biology, and AI for science The postdoc will work at the interface of machine learning, genomics, and scientific computing, contributing both methodological innovation and