Type de poste : Thèse
Sujet : design of core-shell anisotropic particles for the fabrication of artificial nacres
Département : MPH-D3
Profil : PhD profiles include students with a Master 2 degree in Chemistry of Materials, Material engineering or Soft Matter. Know-how on sol-gel chemistry, or polymerisation strategies or additive manufacturing are welcome. The project is purposefully “large” for a PhD, to allow the candidate to explore the directions that he/she finds interesting (optimisation of core-shell particles synthesis, additive manufacturing of nacres, properties of the materials). Know-how in material characterisation techniques (XRD, TGA, DLS/Laser granulometry, electron microscopy) and/or in rheology of suspensions is required. Previous experience in additive manufacturing is a plus. Motivated and independent candidates are welcome. A very good English level (oral, written) as well as strong communication skills are required.
Date limite de dépôt de candidature : 12/05/2026
Date d’embauche : 01/10/2026
Durée : 36 mois
Contact : julien.schmitt@umontpellier.fr
Profil download
Sujet : design of core-shell anisotropic particles for the fabrication of artificial nacres
Département : MPH-D3
Profil : PhD profiles include students with a Master 2 degree in Chemistry of Materials, Material engineering or Soft Matter. Know-how on sol-gel chemistry, or polymerisation strategies or additive manufacturing are welcome. The project is purposefully “large” for a PhD, to allow the candidate to explore the directions that he/she finds interesting (optimisation of core-shell particles synthesis, additive manufacturing of nacres, properties of the materials). Know-how in material characterisation techniques (XRD, TGA, DLS/Laser granulometry, electron microscopy) and/or in rheology of suspensions is required. Previous experience in additive manufacturing is a plus. Motivated and independent candidates are welcome. A very good English level (oral, written) as well as strong communication skills are required.
Date limite de dépôt de candidature : 12/05/2026
Date d’embauche : 01/10/2026
Durée : 36 mois
Contact : julien.schmitt@umontpellier.fr
Profil download
Type de poste : Thèse
Sujet : 3D Printing of Porous Scintillating Monoliths for Radioactive Gas Detection
Département : MPH-D3
Profil : PhD profile and skills acquirement: Through this project, the student will acquire strong skills in nanomaterial synthesis, their integration with innovative 3D printing approaches and their applications to pressing environmental challenges. Candidates should have a Master in Chemistry, Chemical Physics, Materials science or associated fields. Candidates should be highly motivated and willing to work in an international environment. Previous experience in porous and/or luminescent materials is highly recommended. A very good level of English (oral and written) and strong communication skills are required.
Date limite de dépôt de candidature : 03/07/2026
Date d’embauche : 01/10/2026
Durée : 36 mois
Contact : tangi.aubert@umontpellier.fr
Profil download
Sujet : 3D Printing of Porous Scintillating Monoliths for Radioactive Gas Detection
Département : MPH-D3
Profil : PhD profile and skills acquirement: Through this project, the student will acquire strong skills in nanomaterial synthesis, their integration with innovative 3D printing approaches and their applications to pressing environmental challenges. Candidates should have a Master in Chemistry, Chemical Physics, Materials science or associated fields. Candidates should be highly motivated and willing to work in an international environment. Previous experience in porous and/or luminescent materials is highly recommended. A very good level of English (oral and written) and strong communication skills are required.
Date limite de dépôt de candidature : 03/07/2026
Date d’embauche : 01/10/2026
Durée : 36 mois
Contact : tangi.aubert@umontpellier.fr
Profil download
Type de poste : Thèse
Sujet : Artificial Intelligence-Driven MOF Materials Discovery for Autonomous Humidity Control
Département : MPH-D3
Profil : We are looking for a highly motivated Ph.D. candidate with a Master degree or an international equivalent in computer science, physical chemistry, chemical physics, condensed matter physics, statistical physics, theoretical physics, or a related field and a strong interest in materials design. Experience with coding (Python, C, etc.) and force-field- and MILP-based simulations and background in ML/AI tools is an advantage.
Date limite de dépôt de candidature : 30/05/2026
Date d’embauche : 01/09/2026
Durée : 36 mois
Contact : guillaume.maurin1@umontpellier.fr
Profil download
Sujet : Artificial Intelligence-Driven MOF Materials Discovery for Autonomous Humidity Control
Département : MPH-D3
Profil : We are looking for a highly motivated Ph.D. candidate with a Master degree or an international equivalent in computer science, physical chemistry, chemical physics, condensed matter physics, statistical physics, theoretical physics, or a related field and a strong interest in materials design. Experience with coding (Python, C, etc.) and force-field- and MILP-based simulations and background in ML/AI tools is an advantage.
Date limite de dépôt de candidature : 30/05/2026
Date d’embauche : 01/09/2026
Durée : 36 mois
Contact : guillaume.maurin1@umontpellier.fr
Profil download
Type de poste : Post-Doc
Sujet : Artificial Intelligence-Driven MOF Materials Discovery for Autonomous Humidity Control
Département : MPH-D3
Profil : We are looking for a highly motivated Postdoc candidate with a PhD degree in computer science, physical chemistry, chemical physics, theoretical physics, or a related field, with a strong background in training GNN-based ML predictive models (Equiformer, GemNEt, eSEN…..) for accurately predicting Material properties (especially adsorption properties). Experience with ML models applied to materials science alongside background in AI-generative diffusion models (MatterGen, MOFGen…) is a clear advantage.
Date limite de dépôt de candidature : 30/05/2026
Date d’embauche : 01/09/2026
Durée : 24 mois
Contact : guillaume.maurin1@umontpellier.fr
Profil download
Sujet : Artificial Intelligence-Driven MOF Materials Discovery for Autonomous Humidity Control
Département : MPH-D3
Profil : We are looking for a highly motivated Postdoc candidate with a PhD degree in computer science, physical chemistry, chemical physics, theoretical physics, or a related field, with a strong background in training GNN-based ML predictive models (Equiformer, GemNEt, eSEN…..) for accurately predicting Material properties (especially adsorption properties). Experience with ML models applied to materials science alongside background in AI-generative diffusion models (MatterGen, MOFGen…) is a clear advantage.
Date limite de dépôt de candidature : 30/05/2026
Date d’embauche : 01/09/2026
Durée : 24 mois
Contact : guillaume.maurin1@umontpellier.fr
Profil download