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Field
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-based knowledge with machine learning. You will work closely with the Utrecht University team and OpenGeoHub together with other project partners, to develop and implement surrogate and hybrid modelling
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, California 95616, United States of America [map ] Subject Areas: Physics / Astronomy , Astrophysics Computational , Machine Learning Salary Range: Starting at $71,491/year or higher depending on experience
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University of California, Berkeley, Department of Electrical Engineering and Computer Sciences Position ID: University of California, Berkeley -Department of Electrical Engineering and Computer
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About Us We are seeking experts in medical image deep learning to join our team and help develop novel computationally efficient segmentation algorithms. We welcome application from individual with
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deep learning to join our team and help develop novel computationally efficient segmentation algorithms. We welcome application from individual with experience in: Deep learning Medical imaging computing
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. The research will bridge both established and emerging technical expertise within the section, encompassing areas such as FPGA and neuromorphic computing, Edge AI, machine learning, power electronics, and self
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preferred. Proficiency in computational tools such as MATLAB, Python, R, or machine learning applications in immunology is desired. Candidates should have a PhD in Chemistry, Chemical Engineering
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ERC-funded postdoctoral fellow in theoretical developmental biology, using tools from applied mathematics, biophysics, and machine learning A talented and creative researcher is sought to take part
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-scale controllable, and cost-efficient disease models by bringing together experts in physical chemistry, physics, bioengineering, molecular systems engineering, machine learning, biomedicine, and disease
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understanding of artificial intelligence applications and methodologies, such as working knowledge of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI