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unsupervised techniques, time-series modeling, and clustering algorithms. The candidate is expected to lead an effort to prepare generalized ML techniques for data quality monitoring for tasks across multiple
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applications in statistical genetics, developing new theory, algorithms, and scalable implementations. Starting date as soon as possible and upon individual agreement. The fellowship period is three years. A
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minimize coupling of environmental disturbances into detector systems. Develop techniques and algorithms to characterize and remove noise sources. Study noise couplings, high-power operations, and thermal
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optimise deep learning–based models for the quality control and real-time assessment of concrete constituents within in-line production. Develop and train predictive algorithms based on hyperspectral imaging
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of algorithms, data structures, high-performance computing, machine learning and microbiology. The position at the Department of Molecular Biology at Umeå University is temporary for four years to start
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inference. The role will bridge rigorous theoretical work with hands-on algorithm design and development on real-world datasets. The core responsibility is to build and validate federated causal inference
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. In addition, we will explore the possibility of using LIDAR sensing to strengthen the semantic SLAM pipeline. The developed algorithms will be integrated with the perception pipeline developed by our
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the frontier of human-AI collaboration. Rooted in the conviction that algorithms and AI should enhance — not replace — human capability, The Bike Shop develops computing tools that serve as true “bicycles
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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 23 days ago
novel algorithms for visual data analysis, · work with real/synthetic datasets and design data processing pipelines, · integrate the developed algorithms into a comprehensive, functional software
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, and space hardware. This PhD research aims to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust