The opportunity
For our research center in Kraków, Poland, we are looking for a Research Scientist with expertise in Machine Learning for Simulations to enhance simulation-based engineering processes and develop future applications for power grids. You will join a dynamic, motivated and creative team with a broad range of experience and competences. As part of our team, you will combine theory with practice, creating, testing and validating new technologies to enable the energy transition.
How You Will Make an Impact:
- Collaborate with a global team of researchers and engineers in Hitachi Energy business units to identify and solve real-world challenges for Machine Learning (ML) application to enhance simulation-based engineering processes.
- Propose, contribute to and lead research projects related to ML-based improvements to numerical simulations, aiming to develop the next generations of products and systems for power grids.
- Take a multidisciplinary approach to combine Machine Learning with numerical simulations, exploring and applying ML technologies to increase simulation efficiency.
- Working with different simulation software tools, propose and develop ideas and concepts of new approaches for ML-driven simulation pipelines, fostering simulation processes automation and optimization.
- Network in the company and actively distribute your knowledge and expertise.
- Collaborate with local and global external partners including universities, open-source communities, startups and vendors.
- Disseminate your results in scientific publications, patent applications, and technical reports.
We are looking for a Research Scientist with expertise in Machine Learning for Simulations to enhance simulation-based engineering processes and develop future applications for power grids. You will join a dynamic, motivated and creative team with a broad range of experience and competences. As part of our team, you will combine theory with practice, creating, testing and validating new technologies to enable the energy transition.
Your background:
- Master’s or PhD degree or equivalent industrial experience in Computer Science, Applied Mathematics, Engineering or any related field.
- Experience with machine learning, artificial intelligence technologies, in particular in conjunction with numerical simulations. This includes creating surrogate models for simulations, increasing simulation efficiency, developing ML-driven improvements of the simulation process, making ML-based suggestions for input parameters.
- Practical experience with designing, implementing, optimizing and running Machine Learning training and inference pipelines, ideally for industrial applications.
- Understanding of the simulation process and the algorithms underlying simulation software solvers for CFD-type simulations (e.g., MATLAB, Abaqus, Ansys, COMSOL, etc.) and power grid simulations (e.g., PSCAD, etc.).
- Programming experience with relevant Python packages.
- Willingness to mix conceptual activities with hands-on work.
- Willingness to take an interdisciplinary approach, working with experts from domains away from own area of expertise.
- Experience that demonstrates your team-oriented, innovative, and strategic working styles
- Fluency in English, both written and spoken.
In addition the following skills would be beneficial for the role:
- Knowledge and experience spanning multiple disciplines (e.g., electrical, mechanical, fluid dynamics, thermodynamics).
- Experience with optimization and model hyper-parameter tuning.
- Experience in data science and visualization of data.
- Programming experience (e.g. Python, C++, C#, Java, Rust), including software engineering.
- Proven track record of publishing research in reputable scientific journals and conferences.
- Experience in managing research projects, including planning, execution, and reporting.
- Strong communication skills to effectively present research findings to both technical and non-technical audiences.
- Knowledge and experience with products and systems for power grids, green energy and related fields
