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R&D Scientist in Machine Learning for Simulations

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.

Your Responsibilities

  • 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.

Your Requirements

  • 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

地点 Krakow, Lesser Poland, Poland
工作类型 Full time
经验 Experienced
工作职能 Engineering & Science
合同 Regular
发布日期 2025-05-26
参考编号 R0093642

关于日立能源

日立能源是全球技术领导者,致力于构建清洁能源系统,共享低碳美好未来。我们服务于电力、工业、交通、数据中心和基础设施领域的客户,并携手客户与合作伙伴,通过数字化加速能源转型进程,助力实现碳中和的未来。

我们在全球90个国家拥有超过45,000名员工,他们每天都充满目标感地工作,并且利用各自的不同背景打破墨守陈规。我们诚邀你加入我们的全球团队,共同坚守这一简单而深刻的理念:多元化+协作=创新的关键。