PhD opportunities

AI for abnormal and critical grain growth phenomena discremination and avoidance - Application to Nickel base superalloys

Thesis proposal

Area of expertiseMathématiques numériques, Calcul intensif et Données
Doctoral SchoolSFA - Sciences Fondamentales et Appliquées
SupervisorBERNACKI Marc
Co-supervisorHACHEM Elie
Research unitCentre de Mise en Forme des Matériaux
KeywordsDigital twins , Computational Metallurgy, Grain growth , Deep learning strategy
AbstractOne of the European Union's objectives in climate change
consists of reaching net-zero greenhouse gas emissions by 2050.
Such perspective puts the metallic materials industry, as a large
contributor to carbon emissions, under tremendous pressure
for change and requires the existence of robust computational
materials strategies to enhance and design, with a very high confidence degree, new metallic materials technologies with a limited
environmental impact. From a more general perspective, the
in-use properties and durability of metallic materials are strongly
related to their microstructures, which are themselves inherited
from the thermomechanical treatments. Hence, understanding
and predicting microstructure evolutions are nowadays a key to
the competitiveness of industrial companies, with direct economic
and societal benefits in all major economic sectors (aerospace, nuclear, renewable energy, naval, defence, and automotive industry).
ProfileDegree: MSc or MTech in Applied Mathematics,
with excellent academic record. Skills: Numerical
Modeling, programming, proficiency in English,
ability to work within a multi-disciplinary team.
FundingFinancement sur programme européen ou multilatéral