Learning methods for the synthesis of optimal control policies from data

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.

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

7671,64 €