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skills Preferred Knowledge, Skills, and Abilities: * Knowledge of both the theoretical fundamentals and applications of machine learning. * Experience working in multidisciplinary collaborations. * Strong
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are seeking highly motivated Data Science/Machine Learning Research and Development Engineers to join our Computational Intelligence and Visualization Application Department of the Applied Research Laboratory
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fluorescence microscopy (SMLM, SIM), integrating physical-mathematical models, machine learning, and compressed sensing for accurate and efficient reconstructions. Applicants must submit a project implementing
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applications and their underlying systems. Automate machine learning pipelines, monitor performance and costs, and optimize models by using techniques such as LoRA/QLoRA. Establish reusable frameworks
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Introduction to Machine Learning Staff TA - College of Engineering - Integrated Innovation Institute
the disciplines of engineering, design, and business, we can build impactful solutions that create value for real people. Carnegie Mellon University’s Introduction to Machine Learning course is looking for a
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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will
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lead the development of multi-modal MRI foundation models that integrate imaging data and radiology reports. Using advanced deep learning techniques—including vision-language architectures (e.g., CLIP
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that can be used to compare the arrangements • You select suitable algorithms to evaluate the quality of the PPG signals (machine learning, Python) What we offer We offer you a challenging and varied
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/Mey 24G8b1X Deadline for submission of applications: 19 December. TITLE OF THE POSITION OFFERED: Research and Development in Machine Learning for smart cities. SUBJECT AREA: Computer Science. TYPE
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research in Physics-Informed Machine Learning (PIML) for metal additive manufacturing process. This role will focus on developing novel machine learning frameworks that seamlessly integrate physical