R&D Scientist in Discrete mathematic with focus on graph and complexity theories for applied Machine Learning

For our research center in Kraków, Poland we are looking for a Researcher with expertise in graph theory and Machine Learning Model Architecture for the development of the future power grids applications. 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’ll make an impact

• Collaborate with a global team of researchers and engineers in Hitachi Energy business units to solve real-world challenges for software applications for Power systems.
• Propose and develop ideas and concepts of new approaches for graph algorithms with business impact, turn them into prototypes and verify them.
• Exploring Machine Learning Model Architectures for spatial and temporal data coming from power grid sensors.
• Take a multidisciplinary approach to Machine Learning and exploring novel model architectures and training pipelines algorithms yourself.
• 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 background

• Strong academic qualification with an internationally recognized degree at master’s or doctorate level in Computer Science, Mathematics or any related field.
• Interest in and willingness to learn about Power Systems Engineering, Energy Transition and Operations Research.
• Experience with discrete mathematic with focus on graph and complexity theories.
• Experience with designing, implementing, optimizing and running Machine Learning training and inference pipelines.
• Programming experience with relevant Python packages.
• Experience with algorithmic performance optimization for solving large scale graph problems.
• 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:

• Experience with distributed ML model training on prem or in a cloud.
• Experience with model hyper-parameters tuning, including setting custom floating precision for performance boost.
• Experience using automatic differentiation.
• Programming experience (e.g. Python, C++, CUDA).