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and reinforcement. It will build on extensive pavement performance datasets and apply advanced machine learning and statistical methods to generate predictions and assess the effectiveness of different
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, including reinforcement learning and multiobjective stochastic optimisation. These methods will be applied to cooling solutions at the building, district, and city scale, where innovative and energy-efficient
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, computer science, or a related field. Expertise in causal inference and machine learning (in particular reinforcement learning), and strong experience with programming are desired.. Excellent communication and
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, computer science, or a related field. Expertise in causal inference and machine learning (in particular reinforcement learning), and strong experience with programming are desired.. Excellent communication and
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(deterministic, stochastic, robust, reinforcement learning–based) • Systems architecture and design for complex socio-technical systems • Graph theory, network science, and knowledge representation • Agent-based
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have: experience with spiking neural networks and neuromorphic architectures; deep knowledge about our brain and how it makes decisions; coding reinforcement learning algorithms/feedback systems and
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-Service (MaaS) ecosystem. The work will integrate deep reinforcement learning, autonomous agent modelling, and multi-objective optimization to enable predictive simulation, real-time resource management
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. Methodological Areas of Interest Applicants with experience in the following areas are especially encouraged to apply: Optimization (deterministic, stochastic, robust, reinforcement learning–based) Systems
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 4 hours ago
Statistics and Data Science Center at the Institute for Data, Systems, and Society is seeking applicants for a Postdoctoral Associate in the general areas of statistical learning, reinforcement learning
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such as pre-ignition risks, material compatibility, and storage under high pressure. To address these challenges, we will develop novel techniques for provably safe reinforcement learning. This project is