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different model sizes and deployment settings. Apply and advance model compression techniques, including quantization, pruning, knowledge distillation, low-rank adaptation, and related methods. Conduct
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in practice conditions, will be modelled as event-like inputs that perturb the system and whose effects unfold over time. Time-independent predictors, including cognitive abilities, personality traits
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, or artificial intelligence. Knowledge of control systems, system modelling, and data-driven modelling approaches. Proven ability to discretise continuous-time models and implement real-time estimation algorithms
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/Qualifications Strong knowledge of mathematical modeling and numerical methods Experience in computational fluid dynamics and pipeline transport modeling Proficiency in programming and scientific computing (e.g
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computational electromagnetics and electromagnetic simulation techniques. Experience in AI-based RF transistor modelling is highly desirable. Solid knowledge of machine learning algorithms and their application
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-5/P, TROPOMI) with numerical modeling to monitor air quality and environmental risks. Key objectives include developing high-resolution maps for atmospheric pollutants (CO, NOX, SO2, NH3, OCS, CH4
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 15 days ago
/Administrative Internal Number: 25565 POSTDOCTORAL ASSOCIATE, McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, to develop computational circuit models, analytical theory, and
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image analysis and animal models is desirable. We especially welcome applications from enthusiastic candidates with prior knowledge of a broad variety of laboratory research methods and human and/or mouse
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researcher in data-driven modelling of cyber vulnerability risk within the defence domain (1.0 FTE). The Center for Cognitive Science & Artificial Intelligence in the Department of Intelligent Systems
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to the design, development and writing of research and knowledge exchange proposals. Essential Interview Experience of undertaking decision-analytic modelling for public health interventions using modelling