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Field
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processes. Preferred factors: In-depth knowledge of Deep Learning and Large Language Models (LLMs): Practical knowledge with Deep Learning architectures, and in particular, with LLMs. Knowledge of both
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 8 hours ago
been extensively studied in game theory, reinforcement learning, and optimization, their full integration into modern AI systems (particularly in multi-agent deep learning and human-AI collaborative
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within a Research Infrastructure? No Offer Description Introduction As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the
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with a focus on traditional machine learning (shallow learning) and deep learning methodologies. Knowledge of Data Science, including the development of data analysis and visualisation pipelines. 5
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sheet retreat in that region is known to be driven by the ocean, specifically the relatively warm Circumpolar Deep Water (CDW) that drives rapid melting of the the floating ice shelves that form
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multistate-multiphysics deep learning potentials and polarizable embedding methodology to simulate photoinduced charge-transfer dynamics in multichromophoric systems, such as photosynthetic reaction centers
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of genome organisation and metabolic control - with the bold vision of building synthetic life. In this role, you will develop and apply deep learning methods to analyse single-cell modalities, focusing
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to foster a community where different backgrounds, identities, and experiences are valued, and where our people are empowered to thrive through supportive leadership, shared responsibility, and a deep
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Considered Knowledge of CUDA. Knowledge of PyTorch or a similar deep learning framework Experience writing software trigger infrastructure. Experience mentoring more junior researchers Physical Requirements
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the next generation of gas turbine engines. Successful candidates will have a PhD or equivalent in a relevant discipline and experience in the development of machine/deep learning (ML/DL) methods