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Field
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, the appointed candidate will work closely with the line manager to develop novel control algorithms in EAP soft robotics combining Gaussian Predictors, hands-on laboratory experiments and JULIA computing
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information of the data to make a prediction using advanced mathematical tools. This insight opens the door for enjoying the real world. The candidate further develops efficient and robust algorithms
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apply cutting-edge machine learning algorithms, with focus on foundation models and LLMs/agents, to analyze complex biological data. This data includes gsingle cell genomics profiles, spatial data, and
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/ . The post offers an exciting opportunity for conducting internationally leading research on the whole spectrum of novel machine learning algorithms and practical medical imaging applications, aiming
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Rasch tables using time calibrations, and documenting and manualizing the algorithms and related products. The post-doctoral associate will be part time, 12-month employee of the University of Minnesota
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develops efficient and robust algorithms for realistic settings in terms of data and computing resources and collaborates to address major challenges in important applications including marine domain and
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 4 hours ago
Data Exchange Standard (ADES); (2) modernizing short-term impact monitoring through adaptive search algorithms for systematic ranging (Scout); and (3) refining long-term impact prediction via improved
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the coordination of large-scale robot systems (ground and aerial). The ideal candidate will possess hands-on experience with designing and implementing reinforcement learning algorithms, and deploying them onto real
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of the designed algorithms and systems. Help with research presentation works such as high-quality paper writing. Job Requirements: Preferably Bachelor’s degree in Computer Engineering, Computer
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over Morocco. Key Responsibilities: Statistical model development: Led the development of advanced statistical models and machine learning algorithms for forecasting precipitation and temperature in