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Research Intern - Reinforcement Learning

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Huawei

Aug 15

Applications are closed

  • Internship
    Full-time
    Off-cycle Internship
  • Research & Development
    Software Engineering
  • London

Requirements

  • Develop high-impact research output in the field of reinforcement learning, Bayesian optimisation, game theory, multi-agent learning, probabilistic modelling, and/or risk-averse learning
  • Write research-level code capable of testing novel ideas and new approaches
  • Effectively communicate research findings to the team and to the broader community through journal and conference publications
  • Aid in progressing research fields by open-sourcing code
  • PhD in Computer Science or a related field
  • Strong research background demonstrated through journal and conference submissions in any of the following: ICML, NeurIPS, AISTATS, AAAI, UAI, IJCAI, JMLR, Annals of Statistics, and Annals of Probability
  • Hands-on Experience in implementing reinforcement learning, Bayesian optimisation, probabilistic modelling, and/or multi-agent algorithms
  • Knowledge of Python, specifically PyTorch or TensorFlow in addition to OpenAI Gym, GPFlow, Pyro, among others
  • Ability to work in a diverse interdisciplinary team of researchers and engineers with different background

Responsibilities

  • Huawei Technologies Research and Development in London, UK is seeking exceptional candidates to pursue research in various aspects of reinforcement learning and Bayesian optimisation for autonomous decision making under uncertainty. The successful applicant is expected to develop novel contributions allowing the field to move forward in getting it a step closer towards real-world applications. Key research questions include but are not limited to scalable high-dimensional Bayesian optimisation, Gaussian processes, Bayesian neural networks, safe and robust reinforcement learning, multi-agent reinforcement learning, and model-based reinforcement learning. This is an exceptional opportunity for research while collaborating with a diverse team with backgrounds ranging from mathematics and optimisation to probability and game theory. These positions involve theoretical advances and aim to apply innovations in the real world, e.g., in self-driving scenarios, 5g networks, and many other chip design challenges. We aim to publish our work in top-tier conferences and journals, including but not limited to ICML, ICLR, NeurIPS, and JMLR. Our goal is to contribute to an open and transparent research environment by regularly open-sourcing code of our novel discoveries.

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