Job offers

 

Propositions de thèse de doctorat / PhD thesis offer 

  

- Simulation numérique avancée et approches statistiques guidées par la donnée pour la compréhension des points bas en résilience des aciers forgés ( Finite Element Simulation and Data-Driven Statistical Approaches for Predicting the Variability in Forged Steel Resilience )

- start of the PhD : 1/10/2025

- Detailed description & application process : https://www.mat.minesparis.psl.eu/formation/doctorat/propositions-de-sujets-de-these/?id=64556

 

- Generative models for metal microstructures

- start of the PhD : 1/10/2025

- Detailed description & application process :

https://www.cmm.minesparis.psl.eu/wp-content/uploads/2025/04/Gene_poly.pdf

PhD thesis 2021-2024 @ Mines ParisTech / PSL University. Simulation numérique avancée pour la compréhension des points bas en résilience des aciers forgés

 

https://www.mat.minesparis.psl.eu/Recrutements/Theses/Details/Simulation-numA-copy-rique-avancA-copy-e-pour-la-comprA-copy-hension-des-points-bas-en-rA-copy-silience-des-aciers-forgA-copy-s/37111

PhD thesis 2021-2024 @ Mines ParisTech / PSL University. Outils de prédiction de la rupture basés sur le couplage mesure de champs, calculs EFs et machine-learning

https://www.mat.minesparis.psl.eu/Recrutements/Theses/Details/-CONTRAT-DOCTORAL-Outils-de-prA-copy-diction-de-la-rupture-basA-copy-s-sur-le-couplage-mesure-de-champs-calculs-EFs-et-machine-learning/36638

PhD studentship in Probabilisitic Numerics at TU Delft

https://www.tudelft.nl/over-tu-delft/werken-bij-tu-delft/vacatures/details?jobId=2174&jobTitle=PhD%20Position%20Bayesian%20Finite%20Element%20Simulations%20for%20Material%20Modeling

 

 

https://computationalengin.blogspot.com/2020/11/the-bayesian-finite-element-method.html

Stage EDF/Mines/AFH (Paris region) Advanced data assimilation framework for WAAM additive layer manufacturing processes

EDF is currently investigating the capabilities of emerging additive layer manufacturing technologies such as WAAM (wire + arc additive manufacturing). This novel manufacturing process leverages existing welding technologies, whilst promising to allow engineers to build or repair large engineering components in a flexible and reliable manner. As of today, this process is not mature enough to be used for industrial production. This project focusses on establishing a robust numerical pipeline between numerical simulation of WAAM processes on the one hand, and data-rich lab experiments on the other hand. This pipeline will help researchers advance current understanding and control capabilities of this emerging class of additive manufacturing processes. 
 

One of the major difficulties limiting the capabilities of today’s numerical simulators is the multiscale and multiphysics nature of additive manufacturing processes, and WAAM in particular. Predicting how the shape of manufactured parts deviates from nominal geometries proves incredibly challenging, as fine-scale couplings between electromagnetics, thermodynamics, fluid and solid mechanics need to be resolved over large spatial domains and long periods of time. To make simulations possible, it is usually proposed to adopt a simplified, thermo-mechanical macroscopic point of view. However, in order to take unrepresented physics into account, model inputs (heat source models, material deposition models, ...) need to be reliably inferred from appropriately generated experimental data.

The project aims to establish a cutting-edge two-ways experiment-to-simulation pipeline to improve and automatise this inference process. Today’s labs are equipped with high-resolution scanners that may be used to acquire the full geometry of built objects. In turn, we wish to calibrate EDF’s thermo- mechanical model so that the predicted shape deviation from CAD matches that observed in the real-world. It will then be possible to virtually predict the shape deviation from ACD for a new process or component, without manufacturing it physically, thereby paving the way towards virtual design and optimisation of ALM operations.

The technical outlines of the project are as follows.

•   The candidate will construct geometrical algorithms to assimilate point cloud data generated 
•   by 3D scanning of manufactured parts, i.e. to allow inference algorithms to compare real surface profiles to simulated ones. The algorithms will be developed in Python and subsequently interfaced with EDF’s solid mechanics finite element code code_aster. 
• The candidate will develop robust data-assimilation algorithms to tune/learn simplified computational models (of inherent-strain type) based on the 3D-scan data available at EDF. The procedure will be validated against its ability to blindly predict the shape of new WAAM products. 
• The candidate will deploy a data mining strategy to improve the transferability of the calibrated model parameters over a range of manufacturing conditions and part geometries.

The work will be hosted by Mines ParisTech (Centres des Matériaux, http://www.mat.mines- paristech.fr/Research/Scientific-clusters/SIMS/ ), and in partnership with EDF Chatou. The duration of the stage is 6 months minimum, up to 9 months (expected start: winter/spring 2021). The candidate may take part in designing new sets of experiments as part of the project. The work is sponsored by the Additive Factory Hub (AFH), a group of high-tech industries teaming up to advance the state-of-the-art in metal additive layer manufacturing through shared research. The candidate is expected to take an active part in the dissemination of the results in the AFH network. https://www.additivefactoryhub.com/.

Requirements:

• Proven experience in computational engineering & numerical simulation - Strong interest in manufacturing and digital twining 
• Interest in machine learning and data mining 
• Excellent analytical skills   
• Scientific curiosity and strong interest in digital industry

Application and additional enquires:

Send CV and statement of motivation to Pierre Kerfriden, Mines ParisTech pierre.kerfriden@mines-paristech.fr
CC: Sami Hilal, EDF Chatou, sami.hilal@edf.fr , Djamel Missoum-Benziane, djamel.missoum-benziane@mines-paristech.fr 

 

 

 

Keywords:  

Additive Layer Manufacturing, Computational Engineering, Applied Mathematics, Finite Element Method, Data Assimilation, Machine Learning, Industry 4.0

 

https://calcul.math.cnrs.fr/job_080ea3e05f8bd8bbf8160c92894af850.html

PhD Thesis Mines ParisTech / Onera: High performance computing methods for the physics-based simulation of solid propellant

http://www.mines-paristech.fr/Formation/Doctorat/Propositions-de-sujets-de-these/Detail/Methodes-de-calcul-haute-performance-pour-simuler-le-comportement-mecanique-des-Propergols/30640

Applications closed

PhD Project / Cardiff University / University of Luxembourg

Synopsys NE Ltd (https://www.synopsys.com/simpleware.html), Cardiff University and University of Luxembourg invites applications for 2 Early Stage Researcher position (Doctoral Candidate) as part of the Rapid Biomechanics and Simulation for Personalized Clinical Design (RAINBOW) MCSA European Training Network. RAINBOW is funded under the European Union’s Horizon 2020 research and innovation program.

The post holder will be employed on a fixed term (36-month contract) and be principally based at the Synopsys-Simpleware offices in Exeter UK but will also be enrolled as a full time graduate student at either Cardiff University (http://www.cardiff.ac.uk/) or University of Luxembourg and will be undertaking research towards a PhD degree award. The candidate will be expected to spend periods of time in Cardiff or Luxembourg as well as with other partners in the consortium.

The post holders will develop numerical methods at the intersection between machine learning, biomechanical simulations and image processing. In particular, they will contribute to bridging the gap between advanced 3D imaging techniques and physics-based computer simulations in order to improve current capabilities in the area of computer-aided diagnostic and surgical planning. A thorough knowledge of software development is essential.

This is a full time (37.5 hours per week) position on a fixed term basis for a fixed-term of 36 months. Strong programming and analytical skills are required. Same advanced knowledge in computational physics or mechanics woud be a plus. Applications including a CV and a cover letter are required no later than 18/03/2019. Applications should be sent to pierre.kerfriden@gmail.com with title field: "Application RAINBOW PHD XXX", where XXX is the name of the applicant. 

Researcher position in Cardiff. Closing 16 August 2018

Cardiff University invites applications for an Early Stage Researcher position (Doctoral Candidate) as part of the Rapid Biomechanics and Simulation for Personalised Clinical Design (RAINBOW) MCSA European Training Network. RAINBOW is funded under the European Union’s Horizon 2020 research and innovation programme. The post holder will undertake research on “Meta Modelling for Soft Tissue Contact and Cutting Simulation” leading to a PhD degree award. The post holder will develop numerical methods to simulate the deformations of soft-tissues in the context of computer-aided surgery. In particular, he/she will contribute to bridging the gap between advanced 3D imaging techniques and physics-based computer simulations in order to improve current capabilities in the area of computer-aided diagnostic and surgical planning. A thorough knowledge of numerical methods is essential.

Description

Thanks to recent advances in medical imaging and computer tomography, medical surgery is undergoing a revolution. Surgeons have routinely access to 3D reconstructions of patient anatomy that help them perform diagnoses, plan their operations, and may even have access to real-time feedback during surgical operations. However, good-quality-imaging is invasive, and potentially harmful. Within the RAINBOW MSCA network, Cardiff’s research team aims to use physics-based computer simulations to help reconstruct patient anatomy from partial information (noisy snapshots in space and/or time), and to further predict the evolution of biomechanical processes in the future in order to improve diagnoses and planning.

More information and application pages

https://rainbow.ku.dk/open-positions/

Job at Cardiff University

Funded PhD studentship in Cardiff: Machine learning techniques for the optimisation and simulation of Metal Additive Layer Manufacturing process chains

Project Description

The aim of this PhD is to develop new data analytic tools (e.g. machine learning, data mining) to support the understanding, the optimisation and the Multi-scale and multi-physics simulation of metal Additive Layer Machining (ALM) process chains. 

These data analytics tools should meet the needs of the H2020 funded project MANUELA. In particular, to develop “intelligent” feedback loops enabling “online” manufacturing optimisation, design optimisation and tuning of Multi-scale and multi-physics models used for simulations and for the implementation of accurate digital twins of the investigated pilot lines. 

Depending on the type of data available (e.g. temperature maps, machining parameters, localised acoustic information) and on the available controllable factors, various types of process modelling approaches could be used to extract knowledge and features. State of the art modeling, data mining and machine learning tools will be reviewed (e.g. techniques for data regression / classification / clustering such as deep neural network, support vector machine, and dimension reduction learning models, as well as image processing algorithms) and the most relevant will be implemented and enhanced to meet the demands of real data collected at different stages of the pilot line. 

Specialist Equipment / Ressources available: 

Among other standard computing and manufacturing equipment (manufacturing workshop, 3D printers, ) , the student will have access to the following ressources specific to the project needs: 
- Metal Additive Layer Manufacturing Machine 
- High-Performance Computing cluster 
- Machine learning tool kit 
- Indirect access to the H2020 project partners’ equipment (e.g. ALM machines, data analysis/control/simulation softwares) 

Student Required Expertise/skills: 

The work will require the development of software based solutions in the context of ALM pilot lines (real manufacturing, simulation and optimisation), the student should have strong interest and knowledge in the following: 
- Object oriented programming (C++ or equivalent) 
- Data mining/machine learning 
- Additive Layer Manufacturing 

https://www.findaphd.com/search/projectDetails.aspx?PJID=99195

Post-docs in Cardiff

Three post-doctoral fellow positions are available over 5 years at Cardiff University in the framework of an ERC Starting Independent Research Grant with Prof. Stéphane P.A. Bordas and Dr. Pierre Kerfriden, in collaboration with Prof. Karol Miller (University of Western Australia).

The main objective of the projects is to enable faithful simulations of surgical operations in (quasi-) real-time. This objective will be achieved by developing a modern solver based on advanced numerical methods: multiscale methods in space and time reduced order modelling and advanced discretisation techniques. Candidates with experience in either of the following topics will be considered:   
  - model order reduction (e.g. proper orthogonal decomposition - POD)
  - advanced discretisation techniques (extended finite element methods/meshless methods)
  - nonlinear solid mechanics simulations (large-deformation and fracture)
  - multiscale methods (especially in relation to fracture)
  - high performance computing (domain decomposition, preconditioning, multigrid algorithms, solvers, etc.)
  - error estimation and adaptivity (if possible in the context of multiscale or non-linear problems more generally)
   - non-rigid image registration.

The research will be performed in the group of computational mechanics led by Prof. Bordas and Dr. Kerfriden (2 post-doctoral fellows and 10 Ph.D. students) at Cardiff University. Frequent visits to our international collaborators will be organised. The minimum duration of the positions is two years. The applicant is required to have completed, or be close to completion, of a Ph.D. thesis. A significant track record must be demonstrated. We will particularly interested in candidate displaying good academic skills in their fields, an ability to work in research teams (close collaboration with other post-docs, supervision of Ph.D. and master students), and good communication skills.

The interested candidate should send a declaration of interest by email to Prof. Bordas, with a CV and a list of publications attached, and provide the name of three potential referees supporting the application.
contact: stephane.bordas@alum.northwestern.edu pierre.kerfriden@gmail.com (CC for all applications)

Link:
IMAM webpage: http://www.engin.cf.ac.uk/research/resInstitute.asp?InstNo=13 
Job description on Imechanica: http://imechanica.org/node/10386http://imechanica.org/node/10386