The Laboratory for Information and Inference Systems (LIONS) at EPFL is looking for postdoctoral fellows with a strong theory background in machine learning, discrete optimization, information theory, statistics, compressive sensing, or other related areas. Strong coding skills is a big plus.

There are two positions that revolve around the following topics:

1) Bayesian optimization, bandits, and reinforcement learning

We seek to develop online algorithms for Bayesian optimization, as well as related problems such as multi-armed bandits, level-set estimation, and reinforcement learning. The algorithms will be characterized theoretically, and also tested in real-world applications including automated hyperparameter optimization with neural networks and personalized education.

2) Discrete optimization and submodularity with applications to subsampling

We seek to develop techniques for discrete optimization, with submodularity and related concepts playing a key role. These techniques will be targeted at the application of using data in order to optimally subsample for the purpose of performing a given task, such as estimation in compressive sensing or classification in machine learning. Specific applications will also be explored, including medical resonance imaging (MRI) with multiple coils.

LIONS provides a stimulating, collaborative and fun research environment with state-of-the-art facilities at EPFL. Personal initiative and independent research tasks related with the candidate’s interests are also encouraged. The working language at EPFL is English.

Candidates should send their CV, a research statement outlining their expertise and interests, any supplemental information, and a list of at least three references with full contact information to the LIONS Lab Administrator:

Gosia Baltaian ([email protected])

Further information can be found on the group's website, http://lions.epfl.ch/