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against different operation conditions. The position is a part of a research project and prototype battery modules will be produced by one of the partners. The tasks include testing of these prototype
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, with a strong interest in the integration of geospatial Artificial Intelligence (AI) and machine learning. Are you enthusiastic about the chance to combine research in Remote Sensing and AI with teaching
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development of machine learning tools and their applications to medical imaging. Key Responsibilities: The Post Doctoral Associate will apply their technical skills toward the development, implementation, and
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, machine learning, and AI applications in radiology. The research area includes innovative work on developing Deep Learning Based Image reconstruction in CT on Photon Counting Detector CT with work in
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University of California, Los Angeles | Los Angeles, California | United States | about 2 months ago
, the candidate will contribute to other neuroimaging research including novel MRI sequence and imaging marker development to explore the physiological and pathological processes involved in primary and
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measurement techniques and PIV. Familiarity with optics, lasers, image processing and statistical data analysis. Familiarity with flow modelling techniques (CFD) or machine learning for fluid flows. Aptitude
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applications. Research activities may involve geophysical forward modeling, AI-driven geophysical inversion, seismic monitoring and imaging, scientific machine learning, depending on the specific research focus
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processing and statistical data analysis. Familiarity with flow modelling techniques (CFD) or machine learning for fluid flows. Aptitude for team work and excellent communication skills in spoken and written
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written Communication Skills. Certificates/Credentials/Licenses M.D. or PhD Required Computer Skills General office suite, GraphPad-Prism, Endnote, Snapgene, and willingness to learn lab specific software
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machine learning; acoustic signal processing enhanced by ML; human-in-the-loop/active-learning methods; representation learning; analysing sound sequences and vocal interactions; category discovery; Perma