02/06/2020
The Human Resources Strategy for Researchers

PhD contract in the field of Computer science financed during 3 years by the University Clermont Auvergne

This job offer has expired


  • ORGANISATION/COMPANY
    Université Clermont Auvergne
  • RESEARCH FIELD
    Computer science
  • RESEARCHER PROFILE
    First Stage Researcher (R1)
  • APPLICATION DEADLINE
    28/06/2020 00:00 - Europe/Brussels
  • LOCATION
    France › AUBIERE
  • TYPE OF CONTRACT
    Temporary
  • JOB STATUS
    Full-time
  • HOURS PER WEEK
    35 H
  • OFFER STARTING DATE
    01/10/2020
  • REFERENCE NUMBER
    UCA/ANR/006
  • IS THE JOB RELATED TO STAFF POSITION WITHIN A RESEARCH INFRASTRUCTURE?
    Yes

Subject:

Design by AI of innovative multi-physical systems integrating smart materials.

Supervisor:

Yuri Lapusta, Professor.

Laboratory:

Institut Pascal (UMR 6602 - UCA/CNRS/SIGMA), M3G axis (Mécanique, Génie

mécanique, Génie civil, Génie industriel).

Email and phone:

yuri.lapusta@sigma-clermont.fr

+33 4 73 28 80 49

Co-advisor(s):

Frédéric Chapelle, Associate professor.

Abstract (up to 10 lines):

The objective of this thesis is to develop a deep learning and multi-criteria

optimisation method for multi-physical systems integrating smart active materials.

The optimisation algorithm is bioinspired and should be used for the

synthesis of mechanical architectures, while deep learning should enable to

learn by reinforcement the optimal trajectories of a series of representative

tasks. The hybridisation of these two algorithms can be achieved by a sequential

and/or synchronous approach, to be studied. The mechanical architecture

is of a modular nature, each module reflecting a type of smart material

and its properties. The systems thus designed can be physically developed

for validation.

Skills:

We are looking for a master's degree student motivated by Artificial Intelligence,

innovative materials, active systems. The ideal profile includes a first

experience in AI programming.

Keywords:

Deep learning, Bio-inspired multi-criteria optimisation, Smart materials,

Multiphysics active systems.

2/3

Description (up to 1 page):

AI has been used to perform material modelling [1], structure/mechanism

optimisation [2], [3], image analysis [4], automatic programming [5]. The recent

evolutions in deep learning reinforce the interaction capabilities of the

developed systems with their environment [6]. At the same time, the development

of smart active materials reacting to multi-physical stimuli (electricity,

magnetic field, pressure, heat, etc.) [7], [8] has led to the design of innovative

architectures for these systems [9].

These evolutions make it necessary to develop AI algorithms specifically

adapted to the design of multi-physical systems and to the definition of the

tasks they enable to perform. The interest is, for example, to obtain a global

design approach taking into account, during the same optimisation cycle, the

possibilities of physical design and trajectory shape generation for the

achievement of the desired tasks. Such an approach would make it possible

to preserve the interactions between architectural synthesis and task implementation

synthesis during optimisation. The targeted tasks will include follow-

the-leader trajectories, where the system must progress along a path

without deviating from it, as well as scanning a volume, useful for domestic

and medical applications.

The solution will be based on the construction of a hybrid method integrating

two algorithms known for their efficiency in multi-criteria optimisation (bioinspired

such as evolutionary algorithm or particle swarm) and learning by

reinforcement (neural networks). The nature of hybridisation will be studied

during the thesis with determination of the degree of sequential/parallel

synchronisation between the two algorithms. The system architectures will

be modular, each module considering a type of smart material, its shape, and

its physical properties.

Innovative design: multiphysics system + task

Bioinspired optimisation

aggregating candidate solutions

Artificial neural networks

3/3

References (up to ½ page):

[1] A. Mendizabal, P. Márquez-Neila, and S. Cotin, ‘Simulation of hyperelastic materials

in real-time using deep learning’, Medical image analysis, vol. 59, p.

101569, 2020.

[2] F. Chapelle and P. Bidaud, ‘Evaluation functions synthesis for optimal design of

hyper-redundant robotic systems’, IFToMM Mechanism and Machine Theory,

vol. 41, no. 10, pp. 1196–1212, 2006.

[3] R. J. Alattas, S. Patel, and T. M. Sobh, ‘Evolutionary modular robotics: Survey and

analysis’, Journal of Intelligent & Robotic Systems, vol. 95, no. 3–4, pp. 815–828,

2019.

[4] N. Wang and D.-Y. Yeung, ‘Learning a deep compact image representation for

visual tracking’, in Advances in neural information processing systems, 2013, pp.

809–817.

[5] F. Chapelle and P. Bidaud, ‘Closed form solutions for inverse kinematics approximation

of general 6R manipulators’, IFToMM Mechanism and Machine Theory,

vol. 39, no. 3, pp. 323–338, 2004.

[6] N. Sünderhauf et al., ‘The limits and potentials of deep learning for robotics’,

The International Journal of Robotics Research, vol. 37, no. 4–5, pp. 405–420,

2018.

[7] H. Yuan, F. Chapelle, J.-C. Fauroux, and X. Balandraud, ‘Concept for a 3D-printed

soft rotary actuator driven by a shape-memory alloy’, Smart Materials and

Structures, vol. 27, no. 5, p. 055005, 2018.

[8] V. Loboda, A. Sheveleva, F. Chapelle, and Y. Lapusta, ‘A dielectric breakdown

model for an electrode along an interface between two piezoelectric materials’,

Engineering Fracture Mechanics, vol. 224, p. 106809, 2020.

[9] D. Trivedi, C. D. Rahn, W. M. Kier, and I. D. Walker, ‘Soft robotics: Biological inspiration,

state of the art, and future research’, Applied Bionics and Biomechanics,

vol. 5, no. 3, pp. 99–117, Dec. 2008.

How to candidate?

Send an email with CV, cover letter, and grades for the available years of

study (including the first semester of the current year) to the following addresses:

frederic.chapelle@sigma-clermont.fr, yuri.lapusta@sigma-clermont.fr.

 

 
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Offer Requirements

  • REQUIRED EDUCATION LEVEL
    Other: Master Degree or equivalent
Work location(s)
1 position(s) available at
Pascal Institute (IP)
France
Région Auvergne Rhône-Alpes
AUBIERE
63178
Campus Universitaire des Cézeaux TSA 60026 CS 60026 4, Avenue Blaise Pascal

Open, Transparent, Merit based Recruitment procedures of Researchers (OTM-R)

Know more about it at Université Clermont Auvergne

Know more about OTM-R

EURAXESS offer ID: 528421

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