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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 13 hours ago
environments) remains an open frontier. The new MIT Multi-agent AI Postdoctoral Fellowship Program at Schwarzman College of Computing (MIT MAPS) brings together cutting-edge methods in machine learning
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Details Title Postdoctoral Fellow in On-Premise Computing for Autonomous Vehicles (Computer Architecture, Machine Learning and Runtime Systems) School Harvard John A. Paulson School of Engineering
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. The successful candidate will answer questions such as how to assign limited communication resources to train the federated machine learning model efficiently. She/he will investigate realistic scenarios including
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scientist looking to apply your expertise to real-world forecasting challenges in Africa? Machine-learning has the potential to revolutionise weather prediction in Africa, and we are seeking a scientist who
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researcher in natural language processing and large language models to work with a team from multiple disciplines of machine learning and artificial intelligence to develop multimodal large language models
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management. Background in computational modeling and machine learning. Background Investigation Statement: Prior to hiring, the final candidate(s) must successfully pass a pre-employment background
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atmospheric chemistry, air quality, and model development. Basic Qualifications: Ph.D. in atmospheric science and substantial experience in modeling of atmospheric composition. Additional Qualifications
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decision making, while you will be capable to apply machine learning and computational algorithms of social choice. This post is associated with following projects: Embedding EDI in the Distribution
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Responsibilities: Integrate and analyze large-scale multi-omics datasets (genomics, transcriptomics, epigenomics) to derive biological insights Apply statistical and machine learning models to identify cancer risk
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integration and AI models tailored for fish behaviour, health, and stress signal analysis. Investigate and apply novel machine learning and deep learning techniques for pattern recognition, classification, and