Temporary scientific engineer / Numerical optimization, machine learning and statistical methods

Contract type : Fixed-term contract

Level of qualifications required : Graduate degree or equivalent

Fonction : Temporary scientific engineer

Context

The successful candidate will join the Sierra project team (https://www.di.ens.fr/sierra/) within the Inria Paris (https://www.inria.fr/en/inria-paris-centre) to work under the supervision of Adrien Taylor and Aymeric Dieuleveut (http://www.cmap.polytechnique.fr/~aymeric.dieuleveut/), professor at Ecole Polytechnique (Palaiseau).

Assignment

The successful candidate will

  • implement and compare different distributed numerical optimization paradigms for statistical learning
  • accurately study the selected algorithms,
  • participate in developing and maintaining the PEPit (https://pepit.readthedocs.io/) software package, including by adding functionalities related to learning.

 

Main activities

  • Implementation and benchmark of numerical optimization algorithms.
  • Software maintenance and development.
  • Read scientific reports.
  • Write scientific reports.

Skills

Technical skills and level required: master's level in computer science, mathematics, applied mathematics, or engineering.

Languages: English.

Relational skills: easy communication, sociable with an appetite for working in a group.

Additional skills appreciated: rigorous, organized, curious, autonomous, proactive and dynamic.

A specialization in optimization, machine learning, statistical learning or game theory is appreciated. Research experience is a plus.

Benefits package

  • Subsidized meals
  • Partial reimbursement of public transport costs
  • Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
  • Possibility of teleworking and flexible organization of working hours
  • Professional equipment available (videoconferencing, loan of computer equipment, etc.)
  • Social, cultural and sports events and activities
  • Access to vocational training
  • Social security coverage