- Design and implementation of AI-based techniques for the battery management system.
- Data analysis with connection to digital twins.
- Exploration of physics-based approaches using machine learning and reduced-order modelling.
- Coupling with HPC solvers.
- Optimize accuracy and efficiency of these AI approaches to achieve the project KPIs.
- Participate in the project meetings.
- Validate the numerical results with the experimental data.
- Interact with other BSC researchers and all the project partners.
- Education
- The candidate should hold a , computational mechanics or applied mathematics.
- Basic education (BSc/MSc) related to artificial intelligence using machine learning methods is mandatory.
- Basic education (BSc/MSc) related to computational mechanics is highly appreciated.
- Essential Knowledge and Professional Experience
- Deep knowledge on machine learning methods (supervised, semi-supervised, unsupervised and reinforcement learning).
- High knowledge of deep data structures like neural networks (feed forward, convolutional, graph-based, recursive, transformers) and ensemble models.
- 2-4 years of minimum experience with data engineer tasks.
- Good programming skills in Python.
- Good knowledge on common machine learning libraries (Tensorflow, Torch,...).
- Deep knowledge on machine learning methods (supervised, semi-supervised, unsupervised and reinforcement learning).
- Additional Knowledge and Professional Experience
- Good programming skills in other languages (Fortran, C, C++, Julia).
- Good knowledge on reinforcement learning libraries (Rllib, TFAgents,..).
- Competences
- Strong analytical skills.
- Fluent English.
- Ability to work independently and within a team.
- Good communication and teamwork skills.
- The position will be located at BSC within the CASE Department
- We offer a full-time contract a good working environment, a highly stimulating environment with state-of-the-art infrastructure, flexible working hours, extensive training plan, restaurant tickets, private health insurance, support to the relocation procedures
- Duration: Open-ended contract due to technical and scientific activities linked to the project and budget duration
- Holidays: 23 paid vacation days plus 24th and 31st of December per our collective agreement
- Salary: we offer a competitive salary commensurate with the qualifications and experience of the candidate and according to the cost of living in Barcelona
- Starting date: 01/09/2023
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Post-Doc position - Barcelona, España - Barcelona Supercomputing Center
Descripción
Context And Mission
This Post-Doc position offers the chance to participate in a research project coordinated by the Technical Research Centre of Finland (VTT) to advance the batteries field further and contribute to sustainable energy technologies development. VTT is a Finnish research institution leader in developing technological solutions for a sustainable society in terms of energy systems. BSC's participation in this project aims to perform high-fidelity simulations and AI techniques to better understand battery systems with coupled multiphysics phenomena. This numerical data will provide insights into the prototyping design and abuse testing to increase performance and safety.
The successful candidate will work with a team of experienced researchers from the Propulsion Technologies Group and Dual Technologies Group of the CASE Department of the Barcelona Supercomputing Center (BSC, ). Both are multidisciplinary groups with researchers from all disciplines and a strong background in Computational Fluid Dynamics (CFD), advanced software generation with High-Performance Computing (HPC), numerical methods and AI. Specifically, the researcher will be in charge of the co-design, development, implementation and deployment of AI approaches to assess the battery management system. These approaches, using both data and physic-based models, should be closely connected to an efficient HPC-based computational framework that studies the thermo-electro-chemical processes in the battery system. The models developed by the candidate will be verified and validated against the experimental data, gathered from the prototypes provided by the cutting-edge equipment from the project partners.
Key Duties
Conditions
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