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. Experience with high-performance computing clusters or cloud-based Earth observation platforms (e.g., GEE Python API). Experience with airborne lidar data processing and canopy height modelling. We will place
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. Experience with high-performance computing clusters or cloud-based Earth observation platforms (e.g., GEE Python API). Experience with airborne lidar data processing and canopy height modelling. We will place
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“3Ds of 3D: Design, Data quality and Data driven decisions - Advancing statistical methods for human 3D movement analyses of clinical populations”, which aims to develop, evaluate, and implement
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cognitive agent embodied in a humanoid robot Unitree G1 which will collaborate with a human partner to solve a spatial problem (e.g. 3D puzzle). The tasks to be carried out are: (i) scene understanding
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the ability to monitor and measure the machine’s performance. External equipment, such as 3D-scanning, video recordings, bucket weight measurement systems etc. monitoring production are today commonly
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spatial resolution and implement quantitative phase imaging to enable 3D imaging and calibration-free mass concentration mapping. The fellowship holder will then have the opportunity to investigate
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assessment criteria, which will be benefit are: Knwoledge and experience of cloud, monitoring & automation foundation Experience in foundation models and Large Language Models (LLMs). Experience in teaching
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the department of Electrical Engineering research and education are performed in the areas of Communications, Antennas and Optical Networks, Systems and Control, Signal processing and Biomedical
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Resilience and Confidentiality in the Cloud), a EUR 2.5M collaborative project between KTH, Saab, Nvidia, Ericsson, Red Hat, CanaryBit and RISE, building next-generation secure and dependable AI for critical
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subject. Proven experience with X-ray imaging/diffraction techniques, e.g. XRD-CT, 3D-XRD, µ/nanoCT, STXM or similar. Experience in computer programming for data analysis, e.g. Python. Demonstrated ability