Open positions

LA is always looking for excellent candidates with an interest in control. Please note that you must first be accepted to a doctoral program at EPFL before contacting any of the LA faculty.


1 x Postdoc position in data-based control and learning (starting any time from January 2019)

A PostDoc position is available at the Automatic Control Laboratory of EPFL (Switzerland), within the group of Prof. Ferrari Trecate,  in the broad area of data-based control and learning. Candidates with a strong methodological background and motivated by research on theory and algorithms are encouraged to apply.

Qualifications:

  • Ph.D. degree (or close to completion) in Systems and Control, Machine Learning or related fields
  • An established track-record of academic publications in top venues
  • Excellent interpersonal, written, and oral communication skills and ability to write peer reviewed papers

Application procedure:  please email (i) a detailed curriculum vitae and list of publications (ii) the names and contact information of three references and (iii) a sample paper to giancarlo.ferraritrecate@epfl.ch

Deadlines: the call opens from December 2018 and will remain open until an ideal candidate will be found.
Starting date: any time from January 2019. The call will remain open until an ideal candidate will be found. The contract duration is one year, and can be extended up to four years.
EPFL is a top technical university, ranked 12th in the world (2018). The successful candidate can expect a gross salary starting at 81900 CHF, together with other benefits, depending on civil status.

1 x PhD position in data-based control and learning (starting any time from January 2019)

A PhD position is available at the Automatic Control Laboratory of EPFL (Switzerland), within the group of Prof. Ferrari Trecate in the broad area of data-based control and learning. Students with a solid methodological background and passionate of research on theory and algorithms are encouraged to apply. The student will work in a collaborative environment and will learn how to exploit pervasive sensing technologies (such as the Internet of Things) for the design of innovative modeling and control approaches for cyberphysical systems.

Qualifications:
  • a Master degree from a recognised University
  • a strong background in Systems and Control and/or Machine Learning
  • creativity and motivation
  • excellent English language skills

Application procedure:  prospective PhD students must apply to a doctoral program before starting their PhD at EPFL, see http://phd.epfl.ch/prospective.

For the doctoral program on Electrical Engineering (EDEE), application deadlines are December 15th, April 30th, and September 15th.
For the doctoral program on Robotics, Control, and Intelligent Systems (EDRS), application deadlines are January 15th, April 30th, and September 15th.
Fill in the form provided by the doctoral program and indicate your intention to apply to Prof. Giancarlo Ferrari Trecate. Then, email the completed application package to Prof. Ferrari Trecate indicating your interest in the project.
Starting date: any time from January 2019. The call will remain open until an ideal candidate will be found.
EPFL is a top technical university, ranked 12th in the world (2018). The successful candidate can expect a gross salary starting at 51100 CHF, together with other benefits, depending on civil status.

1 x PostDoc position  (the 2 PhD positions have been filled)

Project title:RISK: Risk Aware Data Driven Demand Response

Please see the flyer here for details


  

1 x Post doctoral position at Automatic Control Laboratory, EPFL
Project title: Identification of Distributed Energy Systems
Reference: Dr. Alireza Karimi
General description:
Renewable sources contribute an increasing share of the electrical power, and the concept of distributed generation (DG) is about to completely change the basic architecture of the electric power grid. The new structure includes the interconnections of microgrids that are composed of DG units, loads and energy storage systems.  At the same time, vast advances in computational power and the ability to get high-bandwidth measurements thanks to Phasor Measurement Units (PMUs) open up many new possibilities to identify data-driven multivariable dynamic models for the grids. The system nonlinearity and the lack of persistently excitation signals make the system identification a challenging problem. The objective of this project is to develop new identification methods (inspired from machine learning techniques) that, using the available Big data from the normal operation of a grid, estimate linear (or linear parameter varying) models that can be used for control design.
Application procedure:  prospective candidates should send their CV with a motivation letter to alireza.karimi@epfl.ch. The candidate should have a PhD degree on system identification or related topics. The position is for one year with the possibility of extension.