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Field
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problem solving strategies used in nature and to ground these ideas by fostering deep collaborations with experimental biologists. Most recently, we have been interested in neural circuit computation and
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Stanford's Center for Biomedical Informatics Research, you will have the opportunity to work in close collaboration with clinicians, scientists, and healthcare systems with access to deep clinical data
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participation in proposal writing. Her/his technical proficiency should encompass isotope geochemistry, hydrogeology, and numerical modeling. A deep understanding of serpentinization processes, including water
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floodwater extent and depth, which is critical for supporting emergency-response planning. The successful candidate needs to have knowledge and skills in remote sensing, image processing, machine learning/deep
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will be measured by having published or contributed to papers in top venues (e.g., Nature Science of Learning, Computers and Education, ACM Learning at Scale, Educational Data Mining) and engaging
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Biljecki (National University of Singapore). Your key responsibilities will be: 1. Co-developing the research of work package 1. This work includes, a.o.: Developing deep learning models for the project’s
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in theoretical physics and mathematical physics, and the development of mathematical or physical theory of deep learning. The researcher will perform cutting-edge research in an intellectually
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analytics - Proficiency in Python, deep learning frameworks (e.g., PyTorch, TensorFlow), and large-scale data analysis Additional Application Materials Required Cover letter CV Research Statement or Relevant
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workflows in complex organizational settings. Qualifications: Applicants must have a PhD in Computer Science or related field. Experience in one or more ML domains, such as deep learning, reinforcement
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-world AI deployment, including real-time implementations for end-users. Strong programming skills in Python, MATLAB, and experience with deep learning frameworks such as PyTorch and Isaac Gym are required