Senior Non-convex Optimisation Scientist

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Senior Non-convex Optimisation Scientist

We are looking to hire exceptional numerical optimisation scientists that join our non-convex optimisation team. You will be contributing to our research work and developing non-convex optimisation algorithms that are both theoretically rigorous, yet scalable to real-world scenarios (e.g., to reinforcement learning and large-scale variational inference).

Your responsibilities

  • Conduct theoretical analysis of proposed algorithmic solutions
  • Contribute to research work and publications the team is working on
  • Develop and run large-scale experiments to identify effective solutions
  • Work closely with other researchers and engineers in an Agile team

Skills and experience

  • PhD in Mathematics, Computer Science, Electrical Engineering, or an equivalent degree in a related field
  • Knowledge of convex and/or non-convex optimisation theory and its applications
  • Knowledge of combinatorial optimisation theory and its applications
  • Strong mathematical background in the fields of optimisation, probability theory, and statistical learning
  • Strong knowledge of machine learning algorithms. Knowledge of linking learning and optimisation theory is considered a plus
  • Publication thread in an A-level machine learning and computer science conferences, including but not limited to ICML, NIPS, AISTATS, UAI, AAAI, COLT, FOCS, STOC, SODA
  • Publications in high-impact machine learning and optimisation journals, including but not limited to SIOPT, SIAP, SISC, SINUM, SICON, TAC, and JMLR
  • Knowledge of programming languages, e.g., Python. Familiarity with OpenAI gym and tensor flow is considered a plus

What we offer

Competitive package to match experience consisting of salary and employee stock options, an energetic and fun start-up environment, opportunities for professional development, a team of exceptionally talented colleagues, a position to shape the future of AI.


Cambridge, United Kingdom.

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