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, use of microscopes, data acquisition, inverse problems in imaging, optimization methods and image estimation and in particular knowledge in methods for super resolution structured illumination
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extended to 2 years. The primary focus of this research will be focused on optimization of multimodal treatment of pancreatic cancer (clinical outcome research). In this role, you will be expected to work
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development, particularly in LabVIEW and C++ programming for high-precision measurement systems. As a key member of our team, you will lead the development and optimization of our high-speed dermal atomic force
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genomics, virtual cell models Graph-based neural networks, optimal transport Biomedical imaging, deep learning, virtual reality, AI-driven image analysis Agentic systems, large language models Generative AI
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 6 hours ago
wildland-urban interfaces— across a wide range of climate conditions. Using machine learning methods, we will optimize the weightings of each contributing factor and identify the key drivers of wildfire risk
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d) excellent written, oral communication skills e) strong data analysis skills. Ideal applicants will also have experience with some combination of: a) Machine learning e) code optimization and
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the development and testing of new materials. The work will involve reactor design and setup with gas flow capability and process optimization. Qualified candidates should have a Ph.D. in chemistry, physics
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: Shanghai, Shanghai 200438, China Subject Area: Physics / quantum information theory and atomic experimental physics Appl Deadline: (posted 2025/04/10, listed until 2025/08/01) Position Description: Apply
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documents to support the application 8. Ethical questionnaire (HE ethics checklist + research ethics commitment) 9. A candidate can apply for up to 3 positions. A list with order of preference of positions
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bacterial genomic sequences. The successful candidate will play a key role in developing and optimizing these models, applying them to diverse bacterial use cases, and contributing to the development