Research & Technology Engineer / Artificial Intelligence for Materials Sciences

7 Mar 2024
Job Information

Organisation/Company

Safran

Department

Safran Tech / Materials & Processes Department

Research Field

Engineering » Materials engineering
Chemistry » Computational chemistry
Physics » Computational physics
Mathematics » Statistics
Mathematics » Algorithms
Computer science » Programming

Researcher Profile

Recognised Researcher (R2)

Country

France

Application Deadline

31 Dec 2024 - 00:00 (Europe/Paris)

Type of Contract

Permanent

Job Status

Full-time

Is the job funded through the EU Research Framework Programme?

Not funded by an EU programme

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

Workplace

Safran is an international high-technology group, operating in the aviation (propulsion, equipment and interiors), defence and space markets. Its core purpose is to contribute to a safer, more sustainable world, where air transport is more environmentally friendly, comfortable and accessible. Safran has a global presence, with 92 000 employees and sales of 23.2 billion euros in 2023, and holds, alone or in partnership, world or regional leadership positions in its core markets. Safran undertakes research and development programs to maintain the environmental priorities of its R&T and innovation roadmap.

With its Research & Technology Center located near Paris, Safran seeks to improve its competitiveness in its high technology markets by (1) increasing its capacity to ensure long-term innovation through upstream research and (2) fostering cross-working between the different businesses of Safran, in support of the R&T achieved by the Group's companies. Safran Tech supports production ramp-up while reducing costs, develops innovative solutions to meet environmental goals, and enables Next Generation Engineering with digital technologies.

The role of the Materials and Processes Department of Safran Tech is to identify and apply innovative materials and processes to make products that offer high performance, lighter weight and easier production and maintenance. Engineers develop innovative metallurgy and surface treatment solutions to address the challenges facing upcoming products. Tomorrow's materials will have to be lighter and more heat-resistant to ensure Safran's position in growth markets such as power transmissions and aircraft electrification.

Context

Combinatorial metallurgy is used on a daily basis at Safran Tech to identify innovative high-performance materials. The large variety of products Safran develops, manufactures and commercialises, and their numerous environmental conditions of use (icing, erosion, lightning, extreme temperature…), means engineers face a variety of challenges related to materials design, be they bulk materials (e.g., Ni-, Ti-, Al- and Fe-based alloys) or coatings (e.g., ceramics, metallic…). Combinatorial metallurgy helps engineers to develop new materials by exploiting data mining and machine learning, computational physics/chemistry, small-scale material synthesis and fast characterisation, allowing for shorter overall development cycles and time-to-market.

As a collection of tools from materials sciences, physics, computer science and statistics, combinatorial metallurgy needs specialists from these domains to be deployed to material development or process optimisation projects in support of material experts. The development or assessment of new computational methods to add to this toolkit is another key aspect for combinatorial metallurgy to stay relevant to current industrial challenges in material design. In this regard, the extremely active and fast-moving field of artificial intelligence, notably its subdomain of generative artificial intelligence, holds promises in the field of materials sciences.

In order to expand the team working on combinatorial metallurgy methods, Safran Tech is looking to recruit one Research and Technology Engineer whose skills span both machine learning and materials science and engineering. Applications from both junior and senior candidates with relevant experience will be considered.

Missions include:
- contributing to the development of an integrated computational methodology for the simulation and understanding of material behaviour at the electronic, molecular or microstructure scales;
- building databases and associated machine-learned models to augment material simulations with information extracted from data;
- applying simulations and machine learning for the design of new materials or optimisation of manufacturing processes;
- keeping track of the state-of-the-art;
- being proactive to identify gaps in the current methodology and implement solutions;
- identifying relevant collaborations with academic or industrial partners to improve the methodology by participating in shared national or international programs;
- protecting innovative results by contributing to the redaction of patents;
- where appropriate, disseminating results by publishing or presenting at national or international conferences.

Offer reference
2024-136056

Requirements

Research Field

Engineering » Materials engineering

Education Level

PhD or equivalent

Research Field

Chemistry » Computational chemistry

Education Level

PhD or equivalent

Research Field

Physics » Computational physics

Education Level

PhD or equivalent

Research Field

Mathematics » Statistics

Education Level

PhD or equivalent

Research Field

Mathematics » Algorithms

Education Level

PhD or equivalent

Research Field

Computer science » Programming

Education Level

PhD or equivalent

Skills/Qualifications

The successful candidate holds a PhD (or is studying towards), and possibly previously held one or several successful post-doctoral positions in a related field of physics, chemistry, materials sciences or engineering science and either has experience with (1) computational methods relevant to their respective field such as, but not limited to, Calculation of Phase Diagrams, Phase-Field Modelling, Molecular Mechanics/Molecular Dynamics or Density Functional Theory or (2) Artificial Intelligence, especially Machine Learning and the related fields of data curation, Multi-Objective Optimisation and Multi-Criteria Decision Analysis or (3) Technology Intelligence for collecting and analysing intelligence information from various sources (articles, patents, web, etc.).

Application of candidates holding a PhD in Computer Science and related fields, willing to dive into the world of material design under the guidance of domain experts, are also welcome.

Abilities

- Proficiency in high-level programming languages such as Python or R for building toolchains and analysing experimental data
- Ability to demonstrate a good understanding of issues concerning inverse design problems
- Knowledge in material-specific problems along the Processing-Structure-Properties-Performance paradigm is appreciated
- Good aptitude in oral and written English is a must; applications of non-French speakers are welcome
- Affinity for teamwork and ability to fit in collaborative projects are compulsory
- Candidate should be innovation-oriented and be able to adapt to a multidisciplinary mindset (mechanical and chemical engineering, metallurgy, physics…)
- Interest in academia and industrial relations

Languages

ENGLISH

Level

Excellent

Languages

FRENCH

Additional Information
Work Location(s)

Number of offers available

1

Company/Institute

Safran Tech

Country

France

State/Province

Yvelines

City

Magny-les-Hameaux

Postal Code

78772

Street

rue des jeunes bois

Geofield

Where to apply

E-mail

[email protected]

Website

https://careers.safran-group.com/job/emploi-ingenieur-e-de-recherche-en-metallu…

Contact

State/Province

Yvelines

City

Magny-les-Hameaux

Website

https://www.safran-group.com/careers/working-safran

Street

rue des jeunes bois

Postal Code

78772

E-Mail

[email protected]

STATUS: EXPIRED