PhD Researcher in Physics-Informed AI for Energy Storage (Remote)

Job summary

This position offers a fully funded PhD opportunity focused on the integration of physics-informed artificial intelligence in the field of thermal energy storage. The role aims to innovate decision-making processes in the design and operation of advanced thermal energy storage systems.

Qualifications

• A two-year master's degree (120 ECTS points) or equivalent academic qualification.
• Strong background in thermo-fluids and thermodynamic modeling.
• Proficiency in programming languages such as Python and Matlab, and familiarity with CFD codes like OpenFOAM.
• Experience with energy conversion and storage systems, optimization techniques, and machine learning frameworks (e.g., Pytorch).
• Excellent command of the English language, with strong presentation and scientific writing skills.

Responsibilities

• Conduct high-quality research at the intersection of thermo-fluids science, physics-based modeling, and AI.
• Develop adaptive, physics-informed models that integrate thermo-fluid dynamics and deep learning.
• Collaborate with national and international researchers to validate and improve models and ideas.

Skills

• Proficient in programming and simulation tools (Python, Matlab, Modelica).
• Knowledge of optimization techniques and high-performance computing.
• Ability to work collaboratively in a team-oriented environment.

Education

• Required: Two-year master's degree or equivalent.

Tools

• Programming languages: Python, Matlab, Modelica.
• CFD software: OpenFOAM.
• Machine learning frameworks: Pytorch.