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transport in porous media, and Machine Learning To Apply: Please apply via Academic Jobs Online https://academicjobsonline.org/ajo/jobs/29995 . Anticipated start date is as soon as possible, this is a short
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• Skilled in single-cell/population data analysis (e.g., GLMs, decoding) Preferred Qualifications • Background in machine learning or computational modeling (Bayesian methods, neural networks, etc
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engineering tools, including process modeling, process control, machine learning, and artificial intelligence, to support ongoing proposal efforts in advanced pharmaceutical manufacturing. The successful
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understanding of artificial intelligence applications and methodologies, such as working knowledge of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI
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interpreting wet-lab synthesis data are encouraged to apply and will have opportunities to explore machine learning-guided approaches in chemistry. In addition to excellent research skills, we are seeking
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experience in amino and fatty acid requirements and supplies. Effective use of statistics and modeling tools in support of decision-making tools. Proven ability to work independently and learn continuously
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networks; experience in applying machine learning models and processing imagery from UAS and satellite platforms. Other requirements: Willingness to work irregular hours and in occasionally adverse weather
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machine learning algorithm to improve the modeled surface meltwater in the Goddard Earth Observing (GEOS) model. Advocating and adapting the model based on collaborator feedback is key to the success
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focus on machine learning in the Stanford Center Cancer Cell Therapy at Stanford University School of Medicine. We seek a highly creative and motivated scientist to perform cutting-edge computational
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provides robust learning experiences related to the science and application of HEOR, including cost studies, data analytics, economic models, real-world evidence, services utilization analysis, systematic