Wordcloud of Algorithmics publications 2012-2016

Current vacancies in the Algorithmics group

We are currently offering one post-doc position (see below).  Additionally, we are open to post-doc applications through the LEaDing Follows programme, and to PhD applications through the NWO Industrial Doctorates programme.

Automated decision-making is a key capability of any intelligent system. Intelligent systems (e.g. for traffic planning, energy systems, robots, automated negotiation) must plan ahead, which is particularly challenging when either the future is uncertain, or they interact with many other agents.

To tackle such problems, the Algorithmics group focuses on two lines of research.

  •  We develop new algorithms and solvers for automated planning and scheduling, improve upon existing techniques such as reinforcement learning, Markov decision processes, and mathematical programming, and integrate our results in working prototypes and toolboxes to be used by other researchers and industry.
  • Furthermore, we develop game-theoretical mechanisms which govern the interaction between multiple self-interested systems or users, and provide computationally efficient algorithms to compute equilibria in these interactions.

By tackling these fundamental challenges at the intersection of sequential decision-making, machine learning, and algorithmic mechanism design, we contribute to more efficient energy use, reducing highway congestion, and reducing the cost of asset maintenance in various industries. Our results are published in the internationally leading conferences and journals in planning and scheduling, multi-agent systems, and artificial intelligence.

Post-doctoral researcher position: Machine Learning in Optimisation

We are looking for an innovative post-doctoral researcher to join a cross-disciplinary project funded by NWO, entitled "Real-time data-driven maintenance logistics", which includes several industrial partners such as NS, Philips, and Fokker. This position is an opportunity to gain experience in advanced software development and research and to collaborate with an industrial partner.

The goal of the project is to transition from traditional static maintenance logistics plans to dynamic maintenance logistics policies fuelled by real-time data. Your job will be to investigate different learning algorithms and how to integrate these into the objective functions used in optimisation of maintenance plans. You will implement new learning algorithms that aim to maximise decision-making performance measures such as plan quality instead of traditional measures such as accuracy and predictive performance. These improved learning methods will be integrated into a newly developed real-time decision making framework for maintenance logistics, and published in internationally leading conferences and journals in machine learning, planning and scheduling, and optimisation. 

Requirements 

You have:

  • a PhD in computer science, mathematics, operations research, or a similar field.
  • a keen interest in the combination of machine learning and optimisation.
  • a strong motivation for solving real world problems.
  • an outstanding research and publication record.
  • good analytical and problem solving skills.
  • an excellent command of spoken and written English. 

Duration: 3 years, from early 2018 until end of 2020 (start date is flexible)

Keywords: algorithm design, software engineering, optimization, machine learning

Location: Delft, The Netherlands

The application must include:

  1. a motivation letter
  2. a curriculum vitae
  3. a copy of the degree certificate(s) and transcripts of records from your previously attended university-level institutions
  4. motivation letter
  5. representative publications
  6. contact information for two reference persons

Please submit your application by filling in this online form before March, 31 2018.

For more information about this position, please contact Sicco Verwer, phone: +31 (0)6-18781180, e-mail: s.e.verwer at tudelft.nl