Job opportunities

Centres Inria associés

Inria Paris Centre

Type de contrat

CDD/Fixed-term contract

Contexte

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

Mission confié

<p>The successful candidate will</p>
<ul>
<li>implement and compare different distributed and stochastic numerical optimization paradigms</li>
<li>accurately study the selected algorithms,</li>
<li>participate in the development and maintenance of the PEPit (<a href="https://pepit.readthedocs.io/">https://pepit.readthedocs.io/</a>) and AutoLyap (<a href="https://github.com/AutoLyap/AutoLyap/)">https://github.com/AutoLyap/AutoLyap/</a>) software packages, including adding functionalities related to distributed optimization,<br /><br /></li>
</ul>
<p>&nbsp;</p>

Principales activités

<ul>
<li>Implementation and benchmark of numerical optimization algorithms.</li>
<li>Software maintenance and development.</li>
<li>Read scientific reports.</li>
<li>Write scientific reports.</li>
</ul>

Compétences

<p>Technical skills and level required: master's level in computer science, mathematics, applied mathematics, or engineering.</p>
<p>Languages: English.</p>
<p>Relational skills: easy communication, sociable with an appetite for working in a group.</p>
<p>Additional skills appreciated: rigorous, organized, curious, autonomous, proactive and dynamic.</p>
<p>A specialization in optimization, machine learning, statistical learning or game theory is appreciated. Research experience is a plus.</p>

Référence

2025-09672

Thème

Optimization, machine learning and statistical methods

Domaine d'activité

Scientific computing

Post-Doctoral Research Visit F/M Postdoc in numerical optimization for statistical machine learning

Job opportunities

Centres Inria associés

Inria Paris Centre

Type de contrat

CDD/Fixed-term contract

Contexte

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

Mission confié

<p>The successful candidate will</p>
<ul>
<li>implement and compare different distributed and stochastic numerical optimization paradigms</li>
<li>accurately study the selected algorithms,</li>
<li>participate in the development and maintenance of the PEPit (<a href="https://pepit.readthedocs.io/">https://pepit.readthedocs.io/</a>) and AutoLyap (<a href="https://github.com/AutoLyap/AutoLyap/)">https://github.com/AutoLyap/AutoLyap/</a>) software packages, including adding functionalities related to distributed optimization,<br /><br /></li>
</ul>
<p>&nbsp;</p>

Principales activités

<ul>
<li>Implementation and benchmark of numerical optimization algorithms.</li>
<li>Software maintenance and development.</li>
<li>Read scientific reports.</li>
<li>Write scientific reports.</li>
</ul>

Compétences

<p>Technical skills and level required: master's level in computer science, mathematics, applied mathematics, or engineering.</p>
<p>Languages: English.</p>
<p>Relational skills: easy communication, sociable with an appetite for working in a group.</p>
<p>Additional skills appreciated: rigorous, organized, curious, autonomous, proactive and dynamic.</p>
<p>A specialization in optimization, machine learning, statistical learning or game theory is appreciated. Research experience is a plus.</p>

Référence

2025-09672

Thème

Optimization, machine learning and statistical methods

Domaine d'activité

Scientific computing