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- Delft University of Technology (TU Delft)
- European Space Agency
- Delft University of Technology (TU Delft); Delft
- Delft University of Technology (TU Delft); yesterday published
- MOHAMMED VI POLYTECHNIC UNIVERSITY
- McGill University
- Umeå University
- Universidade do Minho - ISISE
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- Université Toulouse III Paul Sabatier
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description AI-enabled polymer monitoring via multi-sensor intelligent non
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measurement capabilities, in many cases surpassing what is currently achievable from suborbital platforms. Importantly, each mission employs different measurement techniques, applied at varying resolutions and
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different institutions (CNES, CNRS, IRD, UT3). This position is part of the continuous scientific support to the ESA BIOMASS mission, contributing to the development and validation of higher-level forest
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will design and implement novel computer vision and machine learning methods for “sensorized” cameras that extract medically relevant features without transmitting raw video. You will evaluate algorithms
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Research Infrastructure? No Offer Description Job description You will explore hardware/algorithm co-design for NeuroAI, simultaneously taking into account scalability inspired by modern AI workloads, as
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increasing environmental awareness. However, most existing hearing aid algorithms optimize for only one of these objectives at a time, often at the expense of the others. To enhance hearing aid performance
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Change: Leverage high-resolution tactile sensors to handle contacts Impact: Autonomous grasping and manipulation Job description Dexterous manipulation represents one of robotics' most fundamental unsolved
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Challenge: Control robotic dexterous hand during dynamic movements Change: Leverage high-resolution tactile sensors to handle contacts Impact: Autonomous grasping and manipulation Job description
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-criteria, defining their formalization as fuzzy subsets, and characterizing their uncertainty; Integrating Machine Learning algorithms to better account for low-level sensor data (precipitation, wind-driven
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acceleration approaches across different sensing and fault scenarios, thereby informing hardware and architecture trade-offs for future missions. (b) Designing and developing ML and AI algorithms to enhance FDIR