Research Associate in Battery Systems
hace 3 meses
This Post-Doc position presents an opportunity to engage in a pioneering research initiative led by the Barcelona Supercomputing Center (BSC) in collaboration with the Technical Research Centre of Finland (VTT). The focus is on enhancing the field of battery technology and contributing to the advancement of sustainable energy solutions. BSC, a prominent research institution, is at the forefront of developing innovative technological approaches aimed at fostering a sustainable society through improved energy systems.
The selected candidate will collaborate with a team of seasoned researchers from the Propulsion Technologies Group and Dual Technologies Group within the CASE Department. These interdisciplinary teams comprise experts from various fields, all possessing a robust background in Computational Fluid Dynamics (CFD), high-performance computing (HPC), numerical methodologies, and artificial intelligence (AI). The primary responsibility of the researcher will be to co-design, develop, implement, and deploy AI methodologies for evaluating battery management systems. These methodologies, which will integrate both data-driven and physics-based models, are intended to be closely linked to an efficient HPC computational framework that investigates the thermo-electro-chemical dynamics within battery systems. The models crafted by the candidate will undergo verification and validation against experimental data obtained from prototypes supplied by project partners.
Key Responsibilities
- Develop and implement AI-driven techniques for battery management systems.
- Conduct data analysis in relation to digital twin technologies.
- Investigate physics-informed methodologies utilizing machine learning and reduced-order modeling.
- Integrate with HPC solvers.
- Enhance the accuracy and efficiency of AI techniques to meet project key performance indicators (KPIs).
- Attend project meetings and contribute to discussions.
- Validate numerical findings against experimental data.
- Collaborate with fellow BSC researchers and project stakeholders.
- Educational Background
- A degree in computational mechanics or applied mathematics is required.
- A foundational education (BSc/MSc) in artificial intelligence with a focus on machine learning methods is essential.
- A background (BSc/MSc) in computational mechanics is highly regarded.
- Required Knowledge and Experience
- Extensive knowledge of machine learning techniques (including supervised, semi-supervised, unsupervised, and reinforcement learning).
- Proficient understanding of advanced data structures such as neural networks (including feedforward, convolutional, graph-based, recursive, and transformers) and ensemble models.
- A minimum of 2-4 years of experience in data engineering tasks.
- Strong programming skills in Python.
- Familiarity with popular machine learning libraries (e.g., TensorFlow, PyTorch).
- Additional Skills and Experience
- Proficiency in other programming languages (Fortran, C, C++, Julia) is advantageous.
- Knowledge of reinforcement learning libraries (e.g., Rllib, TFAgents) is a plus.
- Competencies
- Excellent analytical abilities.
- Proficient in English.
- Capability to work autonomously as well as collaboratively.
- Strong communication and teamwork skills.
Employment Conditions
- The position is based at BSC within the CASE Department.
- A full-time contract is offered, along with a supportive work environment, state-of-the-art facilities, flexible working hours, comprehensive training programs, meal vouchers, private health insurance, and assistance with relocation processes.
- The contract duration is open-ended, contingent upon the technical and scientific activities associated with the project and budget availability.
- Employees are entitled to 23 paid vacation days, in addition to specific holidays as per the collective agreement.
- A competitive salary will be provided, commensurate with the candidate's qualifications and experience, as well as the cost of living in the region.
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