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Associate will contribute to ongoing projects and have the opportunity to develop independent research aligned with the aims of the ADN lab. Current work focuses on machine learning and multivariate decoding
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Tübingen offers a combination of high-performance medicine and strong research. The goal of the Carl-Zeiss-Project “Certification and Foundations of Safe Machine Learning Systems in Healthcare” is to enable
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methods to improve prediction model generalizability, model fairness, and generalizability of fairness across different clinical sites. The researcher will have the opportunity to use machine learning and
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decision-making for complex infrastructure systems. This position offers an opportunity to contribute to interdisciplinary research at the intersection of civil engineering, machine learning, and systems
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also be expected to be highly collaborative and train and mentor undergraduates in scientific research projects. ESSENTIAL DUTIES & RESPONSIBILITIES INCLUDE: 1) Computer simulations of protein structure
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teaching and learning. Tandon fosters student and faculty innovation and entrepreneurship that make a difference in the world. The Department of Chemical and Biomolecular Engineering at NYU Tandon is home to
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-of-the-art in SLAM, situational awareness, computer vision, machine learning, robotics, and related fields Developing and implementing innovative solutions, validated through real datasets and experiments
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with biological tissue. Duties of the position will include designing optical spectroscopic imaging technology, using numerical simulation and machine learning tools to extract diagnostically-relevant
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operation · Application of artificial intelligence or machine learning in energy or engineering systems 5. Strong programming and modelling skills using relevant tools such as Python, MATLAB
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Postdoctoral Research Associate in Forest Resilience, Climate Change, and Human Health in the Amazon
illnesses. The post holder will also co-supervise a PhD student who will be involved in the same project. This is a highly interdisciplinary project combining forest ecology, remote sensing, machine learning