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. 5. Familiarity with scientific machine learning and data-driven modelling.
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research in formal verification, machine learning and artificial intelligence system assurance. The successful candidate will develop new techniques and tools for analysing, verifying and improving
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– “A human-like progressive learning paradigm for embodied AI: Meta skills for robots by watching and imagination”. They will be required to: (a) identify irreducible low-level sensorimotor units
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in mathematical modelling and Bayesian inference while learning from three collaborating chief investigators. You will also build your publication record and professional networks through seminars
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funds can be used effectively and on time. Research Project: This fellowship will provide an opportunity to learn alongside the Crisis Cooperative Agreement and Innovation (CCI) Team. The CCI Team manages
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The successful candidate will work with Asst. Prof. Shen Shuting on combinatorial inference under a project on "Post-learning inference for near-optimal discrete structures". The main
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scientific leaders and researchers. The group of Wang Zhongjian is looking for postdocs working on scientific computing and machine learning related topics. The funding is currently under a MOE grant entitled
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processes, stochastic control, optimization, or reinforcement learning; • Solid mathematical training and ability to work with rigorous proofs; • Familiarity with Markov decision processes, dynamic
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learning platforms including LinkedIn Learning Progressive, considerate leave provisions to empower your work-life balance and well-being, including leading parental leave, gender affirmation leave and
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to groom the next generation of leaders, thinkers, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and