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Field
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the Future 5G/6G Deployments with Millimeter Wave Integrated Circuit Interfaces Generated by Deep Computer Vision. This project is funded by FCT/MECI through national funds and when applicable co-funded EU
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, Antonia Krefeld-Schwalb, Anne Klesse, Bram Van den Bergh) in the domain of reinforcement learning, deep learning, causal inference, field experiments, consumer behavior, human-AI interactions, behaviorial
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in ML research. Demonstrated ability to work on the interdisciplinary team. Strong programming skills and familiarity with deep learning libraries. Empty heading Preferred Knowledge, Skills, and
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research group in computer vision and machine learning, with seminal results in 3D reconstruction from images, scene understanding, deep learning, optimization, sparsity, etc. IMAGINE is part of
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), computation (bioinformatics, machine learning, statistical analysis), working with animals (radio-tracking, animal handling/sampling), and deep knowledge of evolutionary biology and gerontology. The Norwich
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transcriptomics, graph-based modeling, or deep learning will be considered a strong advantage. Familiarity with Linux/Unix environments, version control systems (Git), and high-performance computing is desirable
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Your Job: In this position, you will be an active part of our "Simulation and Data Lab Applied Machine Learning". Within national and European projects, you will drive the development of cutting
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University of Massachusetts Medical School | Worcester, Massachusetts | United States | about 1 month ago
nanoparticles. The successful candidate will also learn cutting edge deep-sequencing approaches to evaluate off-target editing within the genome. They will have the opportunity to participate in meetings
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data are needed to enhance our understanding of sources, pathways and impact of litter. Cefas is developing a visible light (VL) deep learning (DL) algorithm and collected a large 89 litter category
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frequent cloud contamination. This scale mismatch prevents a coherent representation of radiative–thermal processes at the urban scale. This PhD will develop physics-informed deep learning models for data