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well as develop new algorithms for inverse scattering. These extensions will be employed in a reconstruction process on actual measured data from real-world metrology, in line with the long-term vision presented in
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supervised deep learning algorithms for 3D laser data from forests Developing self-supervised deep learning algorithms for 3D laser data from forests Expand for a wider variety of downstream tasks focused
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advantages. We will provide the necessary hardware and software for the real-time control of the machine, but the candidate will be responsible for developing and implementing the control algorithms. A working
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Are you eager to advance lung cancer diagnostics by developing standardized biomarker assays using state-of-the-art mass spectrometry? Join us as a PhD candidate and contribute to clinically
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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
31.07.2025, Wissenschaftliches Personal The Chair for Efficient Algorithms, led by Prof. Stephen Kobourov, is inviting applications for a fully funded PhD position at the Technical University
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of teaching and research, the FSTM seeks to generate and disseminate knowledge and train new generations of responsible citizens in order to better understand, explain and advance society and environment we
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university, the internationally accredited registrar and classification society DNV, and Cancer Registry of Norway. You will be analyzing and developing algorithms for privacy preserving health registry data
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Your Job: The conventional, manual co-design of algorithms and hardware is slow and inefficient. Our group develops methods and tools to automate the co-design process. The core of this project is
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matter physics, and quantum computing. You will dive deep into the realms of theoretical and computational physics with a focus on the development of algorithms and large-scale numerical simulations. Your
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Exactly: A Bayesian Approach. The project aims to address the challenges in pooling inference, by developing and implementing either exact or asymptotically exact Monte Carlo algorithms in collaboration