Nonsmooth quasi-Newton methods for learning and model predictive control of dynamical systems

Titolo in italiano: Metodi nonsmooth quasi-Newton per l’ apprendimento ed il controllo predittivo di sistemi dinamici

1 Research Collaborator position
(Deadline November 14th, 2024 13:00 )
Fields

 

Numerical optimization, machine learning, model predictive control, system identification

Profile

 

A highly motivated and talented individual is sought to join the team as a research collaborator. The successful candidate should have a background in numerical optimization or machine learning, with a strong experience in computational methods and algorithm development. The candidate must have a proven track record of research excellence, demonstrated by publications in top-tier journals and/or conferences.
Candidates having completed at least two years of a doctoral program will be preferred. They should have experience with numerical optimization techniques, very good programming skills in relevant scientific computing languages (e.g., Python, Julia), and the ability to work both independently and as part of a team.

Activity

 

The researcher will carry out research activities within the scope of the ERC Advanced Grant “  COMPACT”  ( COmputational Model Predictive and Adaptive Control Tools) on the topic of numerical optimization methods for nonlinear system identification. Additionally, the researcher will be possibily involved in laboratory activities to validate the research results on an experimental robotic platform.

Formal requirements

 

  • Master's degree or equivalent in mathematics, artificial intelligence, engineering, or related fields;
  • Enrollment in a doctoral program (preferably for at least two years);
  • A good knowledge of both written and spoken English.

 

Gross amount

€ 19.367,00 per year

Duration

1 year

SSD

IINF-04/A Systems and Control Engineering

 
Project

ERC-2023 ADG - Computational model predictive and adaptive control tools – COMPACT, GA n. 101141351, P0350 CUP D63C24000740006

Job Research Area: 
CSSE
Job Research Unit: 
DYSCO
Job Contract Type: 
Assegno di ricerca
Full call

 

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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)
  • University degree and ongoing PhD (compulsory)

Attachments

  • Your CV in English (compulsory)