At Hitachi Energy, we are at the forefront of research aimed at making power systems more sustainable and environmentally friendly. We are currently seeking an intern to join our efforts in finding alternatives to sulfur hexafluoride (SF6), a greenhouse gas used in electrical equipment. This internship offers a unique opportunity to apply computational methods to a critical environmental challenge in the energy sector.

How you'll make an impact

• Conduct literature surveys and summarize state-of-the-art computational approaches for evaluating alternative insulating gases.
• Develop and implement machine learning models to predict key properties of potential SF6 alternatives, such as dielectric strength, global warming potential, and toxicity.
• Design and optimize algorithms for multi-objective optimization of gas mixtures, considering various parameters simultaneously.
• Implement and refine estimation methods for properties of gas mixtures, including dew points and dielectric strength.
• Analyze and interpret results, presenting findings to the research team and potentially contributing to scientific publications.
• Collaborate with interdisciplinary teams, including chemists and electrical engineers, to validate computational results.

• Currently pursuing a MSc degree in Computer Science, Computational Science, Applied Mathematics, or a related field - official enrollment is essential.
• Background in machine learning, optimization algorithms, and data analysis.
• Proficiency in Python; experience with scientific computing libraries (e.g., NumPy, SciPy, scikit-learn) is highly desirable.
• Familiarity with or willingness to learn concepts in computational chemistry and thermodynamics.
• Experience with version control systems (e.g., Git) and collaborative coding practices.
• Analytical skills and ability to work with complex, multidimensional data.
• Excellent communication skills in English, both written and spoken.
• A team-oriented working style and enthusiasm for interdisciplinary research.