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- Delft University of Technology (TU Delft)
- European Space Agency
- Delft University of Technology (TU Delft); yesterday published
- Delft University of Technology (TU Delft); Delft
- Delft University of Technology (TU Delft); 17 Oct ’25 published
- Delft University of Technology (TU Delft); today published
- Delft University of Technology (TU Delft); 16 Oct ’25 published
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- Tilburg University; 16 Oct ’25 published
- University of Groningen
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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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our industrial partners. You will work in the cyber analytics and CISE labs in the Algorithmics and Software Engineering Research groups at the Software Technology department under supervision of dr
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with the rest of the team, you will build demonstators for the Find2Fix technology at our industrial partners. You will work in the cyber analytics and CISE labs in the Algorithmics and Software Engineering
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Location ESRIN, Frascati, Italy Description Data Quality and Cal/Val Manager for Atmospheric Sensor Missions in the Sensor Performance, Products and Algorithms Section, Earth Observation Mission
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specialist collaborator to guarantee adequate integration of perception and action; advanced motion-planning and control algorithms, continuously refined via robotic digital twins, enable reliable handling
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for ground/space segment threat collection via existing and novel interfaces, including multi-source sensor intelligence collection, fusion and dissemination. 5) Applied forensics for space missions
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, encryption/decryption and compression; use of microelectronics devices (including COTS); implementation, inference, verification and validation of algorithms** on processing hardware platforms for space
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profile, experience and research proposal. Planning and autonomy: The objective is to study the state of the art of planning algorithms that would support onboard autonomous operations of a rover system on
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from multispectral datasets You will contribute to the ongoing development of Machine Learning algorithms for recognition of planetary materials from multispectral datasets. This project combines deep