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The position is placed at the Division of Astrophysics in the Department of Physics to work with David Hobbs on developing algorithms to improve the quality and scientific information that can be
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application! Work assignments Our research projects focus on distributed sensing, hardware-efficient signal processing, robustness and resilience, and communication-efficient decentralized machine learning
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-of-the-art semantic and instance segmentation algorithms for 3D and 4D microtomography data. The project has a particular focus towards analysing fibre-based materials but will also consider other material
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includes signal processing with emphasis on development and optimization of algorithms for processing single and multi-dimensional signals that are closely related to applications and applied research
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in their computation. We want to understand the fundamental principles that permit us to build privacy-aware AI systems, and develop algorithms for this purpose. The group collaborates with several
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environment project, we will develop automated species and community recognition, particularly focusing on pathogenic soil fungi, with help of deep-learning algorithms fed with microscopic image and Raman
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the developmental rules underlying phenotypic variation. The successful postdoctoral fellow will develop and implement an empirical framework that utilizes data-driven algorithms to learn relationships between past
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algorithms. Work duties The main duties involved in a post-doctoral position is to conduct research. Teaching may also be included, but up to no more than 20% of working hours. The position includes
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the research project and the division of Astronomy and Plasma Physics The successful candidate will work on developing and testing algorithms for 3D magnetic field reconstruction. The position will
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distribution and diversity brings to the organization. Please apply by clicking on the "Apply" button at the bottom of the ad. The credentials you invoke must be verified with certification and they must be