137 days ago on emploi.epfl.ch

STI - PhD Positions in Machine Learning @ LIONS

EPFL - Ecole Polytechnique Fédérale de Lausanne

  • Work region
  • Sector
  • Employment type
  • Position

 Please refer to JobSuchmaschine in your application

STI - PhD Positions in Machine Learning @ LIONS

STI - PhD Positions in Machine Learning @ LIONS

PhD Positions in Machine Learning @ LIONS


The LIONS group ( http://lions.epfl.ch) at Ecole Polytechnique Federale de Lausanne (EPFL) has several openings for PhD students for research in machine learning and information processing. Please see our research interests.

We are interested in students with EE, CS, and Mathematics backgrounds. Candidates should directly apply to the EDEE or EDIC doctoral programs and list Prof. Volkan Cevher as a potential host for their PhD studies.

Topics:

1. Guaranteed accuracy for machine learning models of materials compound space
The high-throughput screening of large databases of novel materials candidates constitutes a central goal of the recently awarded MARVEL NCCR grant. Given a training set of compounds with pre-calculated quantum mechanical properties, we seek to construct supervised machine learning models that accurately infer the corresponding properties for similar materials with correctness guarantees.

2. Bayesian optimization / Gaussian process bandit optimization
We would like to build an active learning framework based on Bayesian optimization that adaptively queries an unknown function in order to build an explicit approximation or to optimize the function with theoretical guarantees. For this purpose, we would like to unify key combinatorial structures (e.g., submodularity) with smoothness models (e.g., Gaussian processes) for rigorous guarantees.

3. Discrete optimization with emphasis on submodularity
We would like to exploit the underlying combinatorial structures of decision formulations in order to obtain scale up sampling, inference, and decision systems. For this purpose, we would like to leverage submodularity and develop efficient algorithms with provable guarantees.

4. Scalable convex optimization
We would like to exploit the underlying convex geometry of learning formulations in order to obtain massive speed-ups in learning with convex optimization. The student will work with streaming data models, stochastic approximation, primal-dual smoothing to design new, heuristic-free algorithms with theoretical guarantees.

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 are a big plus.

The working language at LIONS is English.

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.

Starting date: Continuous

For more details please check: http://phd.epfl.ch/application

Informal inquiries should be sent to Gosia Baltaian, gosia.baltaian@epfl.ch
                                                      

                                                                                                                                     STI/afs/27.07.2016