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.
Before starting prepare the application attachments and information as listed below.

Info

  • Personal info and contact info (compulsory)
  • Number of your Identity Document (Passport or Identity Card) (compulsory)

Attachments

  • Your CV in English (compulsory)
  • Identity document (Passport or Identity Card) (compulsory)
Contacts: