Job opportunities

PhD: University of New South Wales, Australia University, Australia
Contributed by Daoyi Dong (
Title: machine learning for quantum estimation and control
This project aims to develop effective estimation and control methods using machine learning for quantum systems. Benchmarking and controlling quantum systems have been an important task in next generation technology. However, efficient methods for the estimation and control of complex quantum systems are lacking. The project will advance key knowledge and provide effective methods to enable us to identify and control complex quantum systems for wide applications arising in this emerging technological revolution. The research outcomes will make an important contribution to accelerating practical applications of future quantum technology. The scholarship provides the following support:
- Work on high quality research projects with the best supervisory teams in world class environments
- $40K a year stipend for four years
- Tuition fees covered for the full 4 year period
- Coaching and mentoring will form a critical part of your highly personalised leadership development plan
- Up to $10k each year to build your career and support your international research collaborations
More application information could be found at:
If you are interested in applying for the scholarship, please submit your application online, or contact A/Prof Daoyi Dong (, Dr Hidehiro Yonezawa ( or Prof Valeri Ougrinovski (  by 20 July 2018.
PhD: University of Lille/CNRS/Schneider Electric, France
Contributed by Mihaly Petreczky (

PhD position at University of Lille/CNRS/Schneider Electric, France.

We are looking for a PhD candidate with a background in one of the following disciplines: machine learning, control theory, systems identification, statistics or probability theory. Knowledge of either machine learning or system identification/control theory is a plus.
The project is a collaborative one between University of Lille, CNRS and Schneider Electric.
The prospective student will spend a significant part of his/her time at Schneider Electric and he/she will be expected to carry out both fundamental and applied research.

Obtaining good models is one of the bottlenecks of the developmental cycle of controllers. While there are many theoretically sound methods for calculating controller based on suitable models of the underlying physical processes, it remains a non-trivial task to estimate those models from data. There are two approaches for obtaining such models. One is system identification, which is a classical branch of control theory which aims at learning dynamical models (difference/differential equations) from data and use these models for decision making (control). Another one is machine learning, especially in deep learning. The strength of machine learning algorithms based on neural networks (deep learning) is that they often generate models which are good at predicting the behavior of complex processes. A disadvantage is that the models produced by those algorithms tend to fall into classes of models for which there exist few theoretically sound algorithms for control design. In contrast, system identification was conceived for producing models used for control, however, existing system identification algorithms tend to work for relatively simple processes. If we could combine these two fields, we might obtain a framework for producing high-quality models of complex processes which are used for control. Such a framework would shorten the developmental cycle of new controllers, make them easier to adapt to new requirements of the clients.

Hence, the goal of this student is expected to validate the obtained results on practical cases studies provided by Schneider Electric.

Contact persons:
Mihaly Petreczky ( CNRS,
Stefan Capitaneanu ( Schneider Electric.
Lotfi Belkoura ( University of Lille

PhD Thesis:  Michelin (Manufacture Française des Pneus Michelin) and University of Poitiers, France
Contributed by Guillaume Mercère ( and Jérémy Vayssettes (
Title: Estimation for vehicle tire-road interactions towards automated driving
A fully funded Ph.D. position is available at Michelin and the Laboratory for Computer Science and
Automation Systems (LIAS), Poitiers University, France. The appointment will be for 3 years.
We are looking for a student who recently completed a Master degree in automatic control or applied mathematics for a Ph.D position in the area of data-driven modeling. The main objective of this PhD thesis is to develop new tools for monitoring road changes on real cars. This project aims at providing new solutions to avoid the aforementioned drawbacks and develop new control algorithms for Automated Driving Systems, leading to an increased safety for the end users. In order to reach this goal, a specific attention will be paid to the development of new algorithms for road friction change estimation, i.e., the development of new tire models in order to (i) estimate possible evolutions of road conditions (ii) study its real-time behavior when road conditions evolve, (iii) develop flexible models for modern ADS design.
Applications (including an application letter, complete CV, list of publications) and inquiries should be addressed to Dr. G. Mercère ( and Dr. J. Vayssettes (
Assistant/Associate Professor of Systems & Control
Contributed by J. W. van Wingerden (
Assistant/Associate Professor position at TU Delft, Delft, The Netherlands
We seek an expert in the field of data-driven modeling and/or control with a solid background in the field of (robust) control engineering and/or nonlinear system identification. Cooperation with other members of the scientific staff and establishing relationships with practitioners are important aspects of this position. You will also contribute to teaching at the MSc and BSc levels. Every new Assistant/Associate Professor is given a tenure-track position.
For more information about this position, please contact dr. J.W. van Wingerden  (
Ph.D. students post-doctoral positions in the area of system identification for periodic systems
Contributed by Roy Smith (
2 Ph.D. and 1 post-doctoral positions at ETH, Zurich
The Automatic Control Laboratory (in the Department of Information Technology & Electrical Engineering) at ETH, Zurich has two open Ph.D. positions and one open Post-doctoral Researcher position.   The research project is focused on modeling, identification and control of systems characterized by periodic behavior.  Both theoretical and application topics will be studied with the application work addressing several areas in the energy domain:  multiple grid energy systems; autonomous kites for airborne wind energy; and thermoacoustic machines.

Qualifications (for the Ph.D. applicant): a solid background in control at the Master's level, together with strong mathematical skills; a strong Masters project in controls, identification, or a related field. Applications must have (or be close to completing) a Masters degree from a recognized university.

Qualifications (for Post-doctoral applicants): a Ph.D. in identification, optimization, or control systems from a recognized university.  Experience in one of the application areas is a benefit but not essential, especially for those with a strong theoretical interest.

The Ph.D. projects will run for four years and the Post-doctoral position is currently funded for three years.  The positions are open from May 2018 and will remain open until filled.  The start date is negotiable but ideally within the next 3 months.  The Automatic Control Laboratory has four faculty members, 10 post-docs and about 30 Ph.D. students.  The working language is English.

To apply:  please submit the following (in PDF format) to Prof. Roy Smith (
 - a current curriculum vitae
 - a one page summary of your research interests and motivation
 - a copy of your most recent transcripts (for Ph.D. applicants)
 - the names and contact information for 2 to 3 references in a position to assess your research potential.

For more details, please contact Prof. Roy Smith ( or have a look at this web-page: