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in numerical analysis, partial differential equations (PDEs), and scientific computing. Solid background in machine learning theories, with specific experience in Physics-Informed Machine Learning
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Informatics (DBMI) at Harvard Medical School and the Yu Lab are seeking a Postdoctoral Research Fellow with experience in machine learning and scientific programming. The candidate will work with a multi
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in empirical analysis using econometric, machine-learning, and language-modeling techniques. Conducting literature reviews and synthesizing existing academic research to support ongoing projects
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requirements PhD in Physics, Applied Mathematics, Computational Science, or a related field Strong background in machine learning, particularly in the development and application of neural networks
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emerging solid-tumor indications. This position is ideally suited for a recent PhD or MD/PhD graduate seeking hands-on training in CAR T cell engineering, patient-sample analysis, and translational
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a PhD in machine learning, math, stats, physics, or some other technical area by the time the position starts. Additional Qualifications Candidates should have significant experience in some area of
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: Preferably PhD in Computer Engineering, Computer Science, Electronics Engineering, or equivalent. Independent, highly analytical, proactive, and a team player; strong verbal and written communication skills
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frameworks for advanced property prediction and analysis of inorganic disordered materials. Carry out machine-learning based first-principle calculations aimed at advancing the understanding defect-based
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research in various areas of mobile network systems, multimedia and AR/VR/XR systems, robotics and machine learning, focusing on fundamental aspects as well as on applications in multidisciplinary contexts
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: PhD degree in Computer Science, Electrical Engineering, or a closely related field Strong research background in computer vision and deep learning Solid experience with multimodal learning, segmentation