Phd Candidate In Multi-Scale Deep Learning Methodology For High Entropy Alloy Discovery
2 semanas atrás
Organisation/Company: CNRS
Department: Institut de physique et chimie des matériaux de Strasbourg
Research Field: Physics
Researcher Profile: First Stage Researcher (R1)
Country: France
Application Deadline: 26 Nov 2024 - 23:59 (UTC)
Type of Contract: Temporary
Job Status: Full-time
Hours Per Week: 35
Offer Starting Date: 2 Dec 2024
Is the job funded through the EU Research Framework Programme? Not funded by a EU programme
Is the Job related to staff position within a Research Infrastructure? No
Offer Description Frequent travel in France is expected during the thesis, the subject involving the Institute of Physics and Chemistry of Materials of Strasbourg (IPCMS), UMR 7504 CNRS and the IFP Energies nouvelles, Solaize site in Lyon. Located on the Cronenbourg campus, the IPCMS is affiliated with the Institutes of Physics and Chemistry of the CNRS as well as the UFR of Physics & Engineering, the European School of Chemistry, Polymers and Materials (ECPM), the Faculty of Chemistry and 'Physique Télécom' of the University of Strasbourg. The IPCMS currently employs 240 staff including approximately 80 researchers and 60 engineers and technicians. The thesis will be carried out within the framework of the Doctoral School 182, in joint supervision at the IPCMS in the 'Surface & Interfaces' department (DSI) in the 'Simulation & Modeling of Complex (Nano)Materials' team under the supervision of Dr. C. Goyhenex and H. Bulou, and at the IFP Energies Nouvelles, in the Digital Sciences and Technologies department, under the supervision of Dr. M. Moreaud.
As part of the program and priority research equipment for the development of innovative materials through artificial intelligence (France 2023), which aims to predict and design new materials and properties using artificial intelligence, the M2P2_HEA consortium (Multi-scale methodology for predicting the properties of high entropy alloys) is working on developing an approach that combines experimental measurements, artificial intelligence, and multi-scale modeling. This approach aims to correlate the structure and thermodynamic properties of high-entropy alloys (HEA) with their catalytic properties, with the goal of predicting a composition and structure suitable for a specific application.
The M2P2_HEA consortium brings together five research teams: IFP Energies Nouvelles, the Institute of Physics and Chemistry of Materials in Strasbourg, the Molecular Electrochemistry Laboratory of the University of Paris Cité, the Laboratory of Condensed Matter Chemistry in Paris, and the SOLEIL Synchrotron.
High-entropy alloys (HEA) exhibit promising characteristics due to their ability to modify the electronic structure and surface reactivity, while also reducing the use of noble metals. The optimization of these alloys requires a deep understanding of the relationship between their structure and their properties. Predictive capability is of paramount importance here, given that conducting a fully experimental study of all possible structures of such an alloy would not only be difficult but also very costly in terms of time and resources.
In this context, the main objective of the doctoral thesis is to design a digital platform that effectively identifies the characteristics (composition, shape, etc.) necessary for an electrocatalyst to exhibit optimal activity for targeted catalytic properties.
Main tasks:
Develop and implement deep neural network models to predict material properties.
Combine AI techniques with multi-scale simulation and atomic modeling.
Collaborate with expert experimentalists to integrate catalytic data and refine models.
Contribute to the identification of new materials with enhanced catalytic properties.
Skills to acquire:
Strong knowledge in machine learning and deep learning.
Experience in neural networks, with a focus on predictive modeling.
Preferably, experience in image processing and materials science.
Excellent analytical skills and ability to work in a collaborative research environment.
This position offers the opportunity to be part of a pioneering project at the intersection of AI and materials science.
When applying, please include the following supporting documents:
Your curriculum vitae should include your most recent qualifications, a summary of your academic and training background to date, the contact details of two academic referees, as well as a list of publications if applicable.
A personal motivation letter, with a maximum length of 2 pages, should outline your motivation to pursue doctoral studies in the research field associated with the proposed topic, emphasizing the specific project mentioned, as well as your relevance for its realization.
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