RESEARCH FIELDEngineering › Materials engineering
RESEARCHER PROFILEFirst Stage Researcher (R1)
APPLICATION DEADLINE08/06/2022 23:00 - Europe/Brussels
LOCATIONFrance › Sophia-Antipolis
TYPE OF CONTRACTTemporary
HOURS PER WEEK35
OFFER STARTING DATE01/09/2022
EU RESEARCH FRAMEWORK PROGRAMMEH2020 / Marie Skłodowska-Curie Actions COFUND
MARIE CURIE GRANT AGREEMENT NUMBER945304
Artificial intelligence for the Sciences” (AI4theSciences) is an innovative, interdisciplinary and intersectoral PhD programme, led by Université Paris Sciences et Lettres (PSL) and co-funded by the European Commission. Supported by the European innovation and research programme Horizon 2020-Marie Sklodowska-Curie Actions, AI4theSciences is uniquely shaped to train a new generation of researchers at the highest academic level in their main discipline (Physics, Engineering, Biology, Human and Social Sciences) and master the latest technologies in Artificial Intelligence and Machine Learning which apply in their own field.
26 doctoral students will join the PSL university's doctoral schools in 2 academic cohorts to carry out work on subjects suggested and defined by PSL's scientific community. The 2021 call will offer up to 11 PhD positions on 12 PhD research projects. The candidates will be recruited through HR processes of high standard, based on transparency, equal opportunities and excellence.
Description of the PhD subject: “AI for AGG”
Context – Motivation
One 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 design, to enhance, to calibrate, 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, and automotive industry). Multiscale materials modeling, and more precisely simulations at the mesoscopic scale also called full-field models, constitute the most promising numerical framework for the next decades of industrial simulations as it compromises between the versatility and robustness of physically-based models, computation times, and accuracy.
In this context, breakthrough numerical front-capturing and front-tracking strategies to model, at the mesoscopic scale, microstructure evolutions of metallic materials all along complex industrial thermomechanical (TM) processing routes have been developed by the PI and his team through the Industrial Chair and Consortium DIGIMU. The outcoming DIGIMU software is now available for the industry, and able for quantitative predictions of microstructure evolutions on material volumes in the range of one mm3, with typical computation times of a few days when performed on a simple laptop. Moreover, recent developments have enable to propose a new front-tracking approach enabling to achieve in 2D the modeling of complex industrial TM paths with the same precision as the most complex state-of-the-art simulations by dividing calculation times by a factor 10 to 100. Such simulations and computational efficiency were a dream ten years ago, a reality now with the current DIGIMU developments.
Abnormal grain growth (AGG) and critical grain growth (CGG) are two well-known metallurgical mechanisms leading to overgrown grains in microstructures without or with stored energy, respectively. As such large grains can be detrimental to the fatigue resistance, it is a critical issue for numerous industrial applications. The understanding and full-field modeling of these mechanisms has greatly been improved in the last decade, mostly through the DIGIMU developments. A statement at the basis of this proposal is that the partners already have the capability of modeling in full-field context the main mechanisms that can lead to AGG or CGG during annealing.
However, from the industrial perspective, the complexity remains that it is generally impossible by looking the final experimental micrography of the microstructure to discriminate easily and confidently the elements involved in the occurrence of these phenomena and their chronology. This aspect explains one of the current difficulty of optimizing the obtained microstructures and why trial-and-error methods often remain the industry norm. Indeed, the causes of AGG or CGG could be numerous when considering the applied TM paths and their effect at the microstructure scale: Heterogeneous or evolutive populations of second phase particles, some initial size advantage, anisotropy of interface properties and texture are the main factors which can lead to AGG. In CGG, the residual stored energy is a major additional ingredient. Finally, the concept of the AI-for-AGG proposal will be to move from our simulations’ predictive nature to an intuitive understanding of final microstructures caracteristics presenting AGG or CGG or even to end up with processing maps exhibiting the windows where the risk of AGG or CGG exists. This breakthrough objective will be based on our capability to build a massive database concerning available experimental data and numerical predictions of AGG/CGG taken into account all the possible causes and the use of neural network-type algorithms to develop new capabilities on AGG/CGG discremination and avoidance.
Such a leap in the models will open the door for industrial partners such as Safran and Aubert & Duval companies to tune numerically TM routes, build microstructure-targeted processing maps and automatically propose new enhanced homogenized models. AI-for-AGG project brings the cutting-edge and exploding strategies of data science, physically-based models, and machine learning at the service of academic and industrial metallurgy. Major advances regarding the concept of digital twins in metallurgy and a worldwide leading position of the AI-for-AGG partners concerning Integrated Computational Materials Engineering (ICME) developments are expected outcomes of the proposal.
Scientific objectives, methodology & expected results
Abnormal grain growth (AGG) or critical grain growth (CGG) can be explained/influenced by numerous attributes of the microstructure and the TM routes. Thus, one can estimate that the quality of the built database, the development of fast computational tools allowing to enrich it easily and the proposed methodology will, for the first time, enable the numerical development of processing maps concerning numerous attributes of the considered post-forming microstructures. Thanks to the efficiency of our front-tracking numerical method, a large physically-based database will be used to train different neural network strategies of the state-of-the-art with various objectives. More precisely, third independent high-gain objectives will be aimed, each of them constitutes a potential breakthrough achievement comparatively to the state-of-the-art.
- First of all, to avoid repetitive front-tracking calculations for data coming from integration points with similar TM paths and initial microstructural characteristics, a trained supervised deep neural network (DNN) will be used to propose final predictions without modeling the microstructure evolutions themselves to limit the number of finite-element front-tracking calculations. Of course, this first foray into neural network-type algorithms will be validated in terms of a trade-off of accuracy for computational time. Moreover these first developments will enable to optimize the structure of the built database and to test different open source python DNN library of the state-of-the-art like Pytorch, Pytorch Geometric Library or TensorFlow.
- The second incursion, which constitutes the main objective of this proposal, will consist in training a DNN to interpret post-process experimental data (2D micrographs) showing signs of AGG or CGG and providing by the industrial partners. Training of this DNN will be done thanks to our physically-based database where the different causes of AGG/CGG will be largely documented by simulations presenting one or more ingredients which likely lead to these phenomena. This development will enable to develop a new academic and industrial capability to interpret automatically the most likely physical ingredients that could explain a final microstructure showing signs of AGG/CGG.
- The latest machine learning algorithm will consist of testing reinforcement learning in the context of process optimization. Typically, the promotion of the AGG/CGG following prescribed TM paths will be defined as a ”mistake” to avoid and some adjustments on the TM conditions as the way to learn from these mistakes. It will thus be possible, at a lower numerical cost, to numerically exhibit windows where AGG or CGG can be avoided. This last strategy will be the riskiest one in terms of required data and possibility to end up with unrealistic adjustments with regards to industrial requirements.
Depending on the needs of the study and the interest of the candidate, short periods of mobility may be planned to visit the industrial partners (Safran, Aubert & Duval).
Marc Bernacki and Elie Hachem
Created in 2012, Université PSL is aiming at developing interdisciplinary training programmes and science projects of excellence within its members. Its 140 laboratories and 2,900 researchers carry out high-level disciplinary research, both fundamental and applied, fostering a strong interdisciplinary approach. The scope of Université PSL covers all areas of knowledge and creation (Sciences, Humanities and Social Science, Engineering, the Arts). Its eleven component schools gather 17,000 students and have won more than 200 ERC. PSL has been ranked 38th in the 2021 Shanghai ranking (ARWU).
- Opportunity to conduct academic research in a top 100 university in the world.
- High-quality doctoral training rewarded by a PhD degree, prepared within Mines Paris - PSL and delivered by PSL.
- Access to cutting-edge infrastructures for research & innovation.
- Appointment for a period of 36 months (job contract delivered by the involved component school of PSL) based on a salary of 2 228 € (gross salary per month).
- Job contract under the French labour legislation in force, respecting health and safety, and social security: 35 hours per week contract, 25 days of annual leave per year (“congés annuels”). Eventual complementary activities may be accepted or proposed by the co-supervisors (maximum of 64h/year for teaching, 32 day/year for specific missions).
- Short stay(s) or secondment in France or abroad are expected.
- An international environment supported by the adherence to the European Charter & Code.
- Access to AI training package, with a strong interdisciplinary focus, together with a Career development Plan.
- Applicants must have a Master’s degree (or be in the process of obtaining one) or have a University degree equivalent to a European Master’s (5-year duration) to be eligible at the time of the deadline of the relative call.
- There is no nationality or age criteria, but applicants must not have resided or carried out their main activity (work, studies, etc.) in France for more than 12 months in the 3 years immediately before the deadline of the call (MSCA Mobility rule).
- Applicants must declare to be available to start the programme on schedule.
For submitting your online application, go to: Artificial Intelligence for the Sciences (AI4theSciences) doctoral program | PSL
The online application should contain the following documents:
- English translated transcripts from the Master’s degree (or equivalent 5-year degree). A copy of the Master’s degree or a certificate of achievement will be required later on for the final registration.
- International curriculum vitae and a cover letter explaining the reasons that lead him/her to prepare a PhD, why he/she applies to this offer and his/her professional project (guidelines will be given to the applicants in order to help him/her in the writing of his/her letter).
- Two academic reference letters.
- A statement duly signed on the mobility rules, availability, and conflicts of interest.
The applicants can only apply to one PhD project among the available ones. Multiple applications of one candidate will automatically make all his/her applications ineligible.
The applications will be analysed by the Management Team for eligibility and completeness. Afterwards, the applications will be reviewed by the Selection Committee. In the pre-selection round (February 2022), applicants will be rated using a scoring system based on 3 criteria (academic excellence, experience, motivation, and qualities). A shortlist of qualified applicants will be interviewed during the selection round (March-April 2022) to further assess their qualifications and skills according to the predefined selection criteria.
All information regarding the applications (criteria, composition of the Selection Committee, requirements) can be found on the website of the programme, in greater detail.
The selection and recruitment processes of the PhD student will be in accordance with the European Charter for Researchers and Code of Conduct of the Recruitment of Researchers. The recruitment process will be open, transparent, impartial, equitable, and merit based. There will be no discrimination based on race, gender, sexual orientation, religion of belief, disability, or age.
CEMEF (Centre for Materials Processing) a joint unit with CNRS (UMR 7635) is a research center of Mines Paris - PSL. It is devoted to the physics, chemical physics, mechanics and numerical modelling of materials processing.
Mines Paris - PSL is a graduate school (Grande Ecole) in Science, Engineering and Economics under the tutelage of the Ministry of Industry. It provides multidisciplinary education (relying upon around 230 academics) to train high level engineers (150/year in “Master’s degree in science and executive education” track and 200 Advanced Master’s degrees/year) expected to fulfil high responsibilities in government bodies and in the industry in France and abroad. This mission is connected to a very strong research activity closely linked to the industry, that has proved its ability to innovate, anticipate and contribute to the evolutions of the socio-economic world in the fields of risk management, public policies, economics et sustainable development among others.
This long-standing link with the industry has lead Mines Paris - PSL to set up several major collaborations (Research and Education Chairs, Framework agreements…) with leading industrial partners such as SAFRAN, EDF, ArcelorMittal or Total to name a few.
On the academic side, it is a full member of Université PSL and as such participates to a wide number of structuring education and research (both applied and more fundamental) projects, especially with two other Grandes Ecoles (Chimie Paris - PSL and ESPCI - PSL). Mines Paris - PSL is also leader or acting member of several major international academic research programs and projects on its own.
Web site for additional job details
Required Research Experiences
YEARS OF RESEARCH EXPERIENCE1 - 4
REQUIRED EDUCATION LEVELMathematics: Master Degree or equivalent
REQUIRED LANGUAGESENGLISH: Excellent
- A MSc or MTech in Applied mathematics with excellent academic record.
- Strong skills in numerical modeling and programming.
- Proficiency in English.
- Ability to work within a multi-disciplinary team.
EURAXESS offer ID: 708921
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