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the Interpretable Machine Learning Lab (https://users.cs.duke.edu/~cynthia/home.html ) for a scientific developer to work in collaboration with other researchers on machine learning tools that help humans make better
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physics, applied mathematics, machine learning, bioinformatics, biophysics, spectroscopy, image processing, ecological modeling, molecular biology, plant physiology, marine biology or an interest in gaining
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-quality robotics research in the areas of robot grasping and manipulation, kinematics and mechanisms, sensing, and human-robot interaction. Within CORE, SAIR focuses on multimodal machine learning for human
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Max Planck Institute for Dynamics and Self-Organization, Göttingen | Gottingen, Niedersachsen | Germany | 18 days ago
holography. We are seeking a highly motivated postdoctor-al researcher to join our multidisciplinary team at the intersection of optics, electronics, machine learning, and atmospheric science. The successful
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, excellent communication skills, excellent computer literacy. Certifications/Licenses Required Knowledge, Skills, and Abilities PhD in life sciences. Experience with proteomics, bioinformatics, mass
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, challenging project. Learning on the job isn't just a benefit – it's a must. Education, Qualifications and Experience Essential Criteria Applicants should hold a PhD in a relevant area of Engineering
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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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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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Postdoctoral Research Associate in Forest Resilience, Climate Change, and Human Health in the Amazon
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, epidemiology
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combine large-scale data, computational methods, and clearly articulated social-science theories to improve our understanding of society. Recent advances in machine learning, natural language processing