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. Areas of interest include—but are not limited to machine learning and deep learning architectures; trustworthy or explainable AI; generative AI and natural-language systems; computer vision and multimodal
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51832010 - mohsen.assadi@uis.no Raoof Gholami - Professor - +47 51831406 - raoof.gholami@uis.no Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/291112/phd-fellowship-in-sustain
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Research Infrastructure? No Offer Description TASKS/ROLE * conducting research under the project Design-ready forward and inverse surrogate modeling of high-frequency structures using deep learning and
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, applied mathematics, neuroscience, or a related field. Proven experience in machine learning, deep learning, generative AI and data mining. Strong programming skills (e.g., Python, R, MATLAB, or similar
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processing. A5. Knowledge of advanced machine-learning or deep-learning approaches for biosignal analysis. A6. Understanding of technologies relevant to real-world deployments, such as embedded systems design
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https://pubmed.ncbi.nlm.nih.gov/36596869/ Research area: Cancer biology Keywords: lymphoma, CLL, lncRNA, microenvironment Funding of the PhD candidate: Part-time salary (min. 0,5 FTE) on EHA grant/AZV
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experimental design. Proficiency with machine vision and deep learning techniques, including image segmentation, landmark placement and metric learning, for the automation of phenotypic analysis of large image
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. The BlueMat project ?Multiscale Operando Super-Resolution Imaging of Water Imbibition in Hierarchically Porous Materials? aims to establish methods for super-resolution imaging using deep learning. You will
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artificial intelligence and/or machine learning, digital health, wearables, etc. Contribute to Epidemiology curriculum development, teach epidemiologic methods as part of the Epidemiology PhD methods sequence
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Your Job: Join our team as a dedicated scientist and contribute to our exciting research projects. Our work focuses on models and algorithms for supervised and unsupervised learning. We devise deep