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data and IT infrastructure. Combination of different algorithms to test multimodal predictive modelling. Compilation and presentation of data orally at seminars and conferences, as well as independent
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of the project is to develop knowledge, models, and algorithms for physics‑informed autonomous control of heavy machinery in uneven and deformable terrain. Specific project tasks include fundamental studies
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and tomographic radar capabilities. Our team is responsible for the algorithms which derive the biomass data product. The post-doc project is about extending the biomass algorithm to also include data
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objective of the project is to develop knowledge, models, and algorithms for physics‑informed autonomous control of heavy machinery in uneven and deformable terrain. Specific project tasks include fundamental
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, modulation classification, sensing, and adaptive spectrum optimization in diverse operational environments. Your work will focus on modeling and algorithmic aspects related to the development of highly
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., microfluidic channel optimization, polarization-dependent scattering studies, spectral imaging implementation, or algorithm development). Planning experimental campaigns, simulations, and modeling efforts
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algorithmic aspects related to the development of highly accurate, efficient, and robust AI models capable of operating effectively within complex and dynamic radiofrequency spectral landscapes, accounting
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isolation algorithms and data-driven classifiers. As postdoc, you will principally carry out research. You are expected to actively publish and present results in scientific journals and conferences. A
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performance should improve over time as more data becomes available. The diagnostic conclusions will be presented to an operator using a combination of AI-based fault isolation algorithms and data-driven
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We are seeking a postdoc to co-design efficient and realistic simulation algorithms for noisy quantum circuits in superconducting hardware, combining quantum modeling with hardware-aware performance