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machine learning-based model to map satellite retrievals to ground based air pollutant concentrations Conducting error assessment on the derived concentration data Implementing new observational data
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of AI approaches at different levels of the model chain, i.e. by implementing end-to-end learning frameworks that link data, forecasts, and operational decisions, while incorporating diverse contextual
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learnings and inform a classroom implementation and professional development model. This role will be based in California, with travel throughout the San Francisco Bay Area (with most work in the first year
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learning on and off campus, including a growing emphasis on international experiences through short- and long-term study abroad. Students also have access to a robust program of applied research and outreach
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. This includes, but is not limited to: machine learning algorithms, formal proof assistants, and large language models. Areas of interest for possible collaborations include but are not limited to: topological
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Models Basic Qualifications: A Ph.D. or equivalent degree in Machine Learning, Computer Science, Electrical Engineering, Geophysics, Applied Mathematics, or a closely related field. Demonstrated strong
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The successful candidate will develop generative machine-learning methods for amorphous molecular thin films — the supramolecular structures that govern the performance of organic-electronic materials
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distributed training or inference of large AI models on GPU clusters. Experience with modern AI frameworks for large-scale machine learning, such as PyTorch, JAX, NVIDIA NeMo, Megatron-LM or DeepSpeed
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, autonomous, and fast. We're seeking a scientist-engineer hybrid to design the learning layer of our scientific agent platform. You will design the environments, rewards, and domain-specific models that enable
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https