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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 8 hours ago
Posting Description POSTDOCTORAL ASSOCIATE, Mechanical Engineering,will work under the direction of Prof. Sherrie Wang to develop deep learning methods for hyper-local weather forecasting with
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disease to join our CDC-funded project team to strengthen partnerships with public health in the Ohio River Valley and develop predictive models and hazard maps that inform public health decision-making
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mapping approach developed in WP1. The main outcome of this research is a rich, comparable, multi-modal data set, produced in collaboration with local communities across field sites. This data will be used
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designed to meet these needs and co-develop a protype that combines weather, climate and local economic data that can be managed and maintained by local stakeholders. Working pattern: This post is full-time
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, ensuring the preservation of geometric and topological properties. Conduct a literature review on representation learning (SSL, Geometric DL) applied to the sciences. Develop and implement tokenization
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into solutions for local, national, and global communities. As it was at our founding in 1766, the heart of our mission is preparing students to become productive members of society and good citizens of the world
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training graduate students, technicians, and other laboratory members, as needed Participate in the writing and development of grant proposals to support research Present research findings at local and
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environment is a fundamental human right. We believe that ensuring a prosperous global future depends on the ability to support local people and communities everywhere. By working in and across multiple
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circuit platforms. This work includes the development and theoretical study of novel circuit architectures, aimed at improving coherence and enabling new approaches to quantum computation. The Postdoctoral
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background in zoonotic and vector-borne disease to join our CDC-funded project team to strengthen partnerships with public health in the Ohio River Valley and develop predictive models and hazard maps that