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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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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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plan (e.g., microfluidic channel optimization, polarization-dependent scattering studies, spectral imaging implementation, or algorithm development). Planning experimental campaigns, simulations, and
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theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise
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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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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
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in conducting research on AI, algorithmic governance, automation, digitalisation and the implications for the public sector Experience applying analytical and methodological research approaches
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Design and implementantion of automatic calibration techniques for fast tune-up Implementation and benchmarking of quantum algorithms Who we are looking for The following requirements are mandatory: A
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, experience working with the PyTorch framework, documented ability to develop algorithms and implement them in efficient code, and experience in statistical modeling, optimization or numerical methods, as
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Are you interested in developing machine learning algorithms that provably help us make better decisions? Join us as a post-doc in the Division of Data Science and AI, Department of Computer Science