123 algorithm-development-"Multiple"-"Simons-Foundation"-"Prof"-"UNIS"-"University-of-Warwick" positions
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conditions, identification of vulnerabilities, and development of resilience enhancement strategies. Contribute to the design, development, and implementation of new models, methods, and algorithms
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development, human-computer interaction, data analytics, user experience design, remote monitoring systems, energy optimization algorithms, and environmental impact modeling. Human-centric AI-driven sanitation
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Missouri University of Science and Technology | Rolla, Missouri | United States | about 23 hours ago
for Infectious Diseases Research. This position will be responsible for performing various duties necessary to further research on multiple grants. Responsibilities: Responsibilities will include developing
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discovery by conducting data-driven experiments and developing machine learning models on diverse biomedical data modalities. Responsibilities Data processing across multiple biomedical domains (image, speech
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| Mechanical and Aerospace Engineering This Researcher 3 in the Department of Mechanical and Aerospace Engineering reports to Dr. Jami Shah in Digital Design & Manufacturing Lab. This position develops
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and application of fast solvers for Maxwell’s equations and nonlinear inversion algorithms that we have already developed in a previous PhD project. In addition to electromagnetic geophysics
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· Gender-friendly environment with multiple actions to attract, develop and retain women in science · 32 days’ paid annual leave, 11 public holidays, 13-month salary, statutory health insurance
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research initiatives that analyze the impact of AI technologies on social systems and design innovative methods for adapting economic and social governance to AI development. A special focus will be
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tuition fees. This PhD project in the area of autonomy, navigation and artificial intelligence, aims to advance the development of intelligent and resilient navigation systems for autonomous transport
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integrates machine learning and statistics to improve the efficiency and scalability of statistical algorithms. The project will develop innovative techniques to accelerate computational methods in uncertainty