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Job Requirement Have relevant competence in the areas of Deep Learning/Computer Vision. The experience in diffusion models is a plus. Have a PhD degree in computer science/engineering or related
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Engineering, Mechatronics, Computer Science, etc. Strong background in AI, Large Language Model, autonomous driving, deep learning, interaction modelling, prediction, robotics and automation. Candidates having
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in developing bioinformatics pipelines (Python/R/Bash) for genomic (NGS) and medical imaging data analysis, implementing artificial intelligence and deep learning techniques. - Experience in developing
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interested in using AI to unravel the mysteries of the brain? Do you want to perform cutting-edge NeuroAI research and leverage deep learning to understand human vision? Then check out the vacancy below and
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implement computer vision pipelines for crop monitoring, plant stress detection, and disease identification in greenhouse environments. Apply machine learning and deep learning models (semantic segmentation
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within the centre. You will be part of a network of young researchers in deep learning in the Visual Intelligence Graduate School https://www.visualintelligence.no/about/vigs Jarli & Jordan/ UiO via
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, with research expertise in geospatial AI, deep learning foundation models, hydrology, river science. Candidates will need to have completed their Ph.D. or have it completed by the start of employment
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description As a PhD candidate, you will: - Develop and train deep-learning
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evaluate deep learning models for MRD detection and characterization Collaborate with multidisciplinary teams across Dana-Farber Cancer Institute, the Broad Institute, and more Mentor and guide junior staff
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University of Massachusetts Medical School | Worcester, Massachusetts | United States | about 3 hours ago
expose the successful candidate to cutting-edge genome editor engineering approaches and the delivery of these reagents in vivo via AAV or lipid nanoparticles. The successful candidate will also learn