Open positions
PhD positions in Machine Learning
Laboratory for Information and Inference Systems at Ecole Polytechnique Federale de Lausanne (EPFL) is currently looking for multiple PhD students in Machine Learning.
There are two positions that revolve around the following topics:
We would like to develop robust reinforcement learning techniques based on optimization and sampling techniques. Inverse reinforcement learning and imitation learning are also of interest.
Besides, candidates’ experience on learning theory, sampling, and statistics are also encouraged.
The LIONS lab provides a fun, collaborative research environment with state-of-the-art facilities at EPFL, one of the leading technical universities worldwide. EPFL is located in Lausanne next to Lake Geneva in a scenic setting with excellent transport connections. The working language at EPFL is English.
Successful applicants need to be highly motivated, excellent students with a solid background in information theory, optimization, computer science, or applied mathematics. Advanced coding skills is a big plus.
Candidates should directly apply to the EDEE or EDIC doctoral programs and list Prof. Volkan Cevher as a potential host for their PhD studies.
For more details please check: http://phd.epfl.ch/application.
The working language at LIONS is English.
Starting date: Continuous
Informal inquiries should be sent to Gosia Baltaian, [email protected].
Postdoc positions
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 two topics:
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.
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.
We seek to develop gradient and linear minimization oracle based algorithms for convex and non-convex problems. In particular, we are interested in the marriage of online and offline optimization, universal adaptation, and storage optimal solutions to difficult training problems that range from semidefinite programming to neural network training.
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.
For the postdoctoral positions, candidates should have or be close to finishing a PhD degree in electrical engineering, computer science, applied mathematics, or a related field. 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])
EPFL STI IEL LIONS
ELD 244 (Bâtiment ELD)
Station 11
CH-1015 Lausanne
Informal enquiries can also be sent to Gosia Baltaian, [email protected].