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, emissions, and productivity. Decision-Support & MCDA Implement a machine-learning-driven multi-criteria decision analysis to rank and select optimal decarbonization pathways. Collaborate with industry and
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, Computer Science or related fields (for PhD); Doctorate in Physics, Computer Science or related fields (for Post-Docs). The positions are funded via the Cluster of Excellence (Machine Learning for Science), the ERC
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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
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faculty and students are at the forefront of the high-tech start-up culture in New York City. The NYU Tandon School of Engineering is deeply committed to excellence in teaching and learning and fosters
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conducts research on the application and the impact of digital technologies like DLT/Blockchain, Digital Identities and Machine Learning/AI on organisations from both the private and public sectors
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 23 hours ago
of patient care, education, and research. We strive to promote faculty, staff, and learner development in a diverse, respectful environment where our colleagues demonstrate professionalism, enhance learning
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independently, has a passion for AI and its applications, and is willing to learn new technologies. The candidate should have a PhD in Computer Science or a closely related field. Relevant background and skills
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techniques to challenging biological systems. Requirements: Ideal candidates should hold an excellent PhD in structural biology, biochemistry, biophysics or in an equivalent area. Experience in membrane
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lab in Stanford’s Psychiatry Department, led by Neir Eshel, MD, PhD. We are looking to hire curious and ambitious postdocs to join our team. Lab projects focus on the neural circuitry of reward-seeking
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on applying, developing and implementing novel statistical and computational methods for integrative data analysis, causal inference, and machine/deep learning with GWAS/sequencing data and other types of omic