Learning methods for the synthesis of optimal control policies from data

1 Research Project Grant position
(Deadline March 17th, 2020 12:00 )
Fields

Machine learning, nonlinear system identification, feedback control, stochastic optimization

Activity

Development of novel stochastic gradient descent methods for learning nonlinear optimal feedback policies from input/output data collected from a dynamical system, such as policies defined by neural network structures.

Profile

IMT Lucca invites applications from candidates with expertise in machine learning and feedback control design, preferably with an undergraduate background in mathematics or engineering, and with programming skills in Python and MATLAB.

Formal requirements
  • Master Degree in Mathematical Engineering or fields related to the subject of this call
  • Excellent knowledge of English, both written and spoken.
Duration

6 months

Gross amount

7671,64

Job Research Area: 
CSSE
Job Research Unit: 
DYSCO
Job Contract Type: 
Borsa a progetto - Project fellowship

Application

Apply ONLINE only.

Guidelines for applying through the PICA platform (Italian | English).
Before filling in the application form, please read thoroughly the full call and collect all the files you may need:

Info

  • Personal info and contact info (compulsory);
  • University degree (compulsory).

Attachments

  • The scanned copy of a valid identity document (Passport or Identity Card - compulsory);
  • Your CV in English (compulsory).
Contacts: