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Field
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to, atmospheric science, environmental engineering, earth observation, and computer science. Strong programming skills and experience with machine learning applications in geoscience are highly desirable
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approaches such as non-coding CRISPR screens, the Massively Parallel Reporter Assay (MPRA), saturation mutagenesis, and synthetic sequence design, alongside machine-learning models of regulatory grammar
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Paravision Micro-CT, optical imaging, photoacoustic imaging, PET, or SPECT Artificial intelligence or machine learning for biomedical image analysis Python, MATLAB, C++, or similar scientific programming
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 2 months ago
, computer engineering, or a related discipline. Strong motivation, scientific curiosity, and commitment to scientific excellence. Experience in one or more of the following areas: electronics and
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, biomedical engineering, medical technology, optics, mechatronics, computer engineering, or a related discipline. Strong motivation, scientific curiosity, and commitment to scientific excellence. Experience in
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systems. • Develop and implement predictive models for packaging performance using machine learning approaches and physics-based simulations. • Investigate logistics optimization strategies for packaging
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will engage in research that includes applied machine learning, systems building, and Internet measurements. Key Responsibilities: Contribute to driving and developing the lab's research directions and
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Difallah in the Division of Science, New York University Abu Dhabi, seeks a Post-Doctoral Associate or a Associate Research Scientist to join a lab focused on applied machine learning. The successful
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and associated environmental impacts. Contribute to short-term (2026 to 2030) and long-term (2030 to 2050) verticalisation forecasting models based on machine learning, and to their validation against
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materials using molecular simulation and artificial intelligence. The position will support startup-funded research in the Jiang Group focused on integrated modeling and machine learning for energy and