PhD: Modeling and Experimental Validation of Thermal and Mechanical Energy Storage

CIC energiGUNE is looking for candidates to carry out a doctoral thesis in the Thermal Energy Storage and Conversion Area, focused on the modeling and experimental validation of thermal and mechanical energy storage units within the framework of the European project COMPETENT.

The researcher will work within an active international European consortium, and their results will contribute to the development of a larger-scale project demonstrator.

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For more information on CIC energiGUNE's research activities, visit our website https://cicenergigune.com/es

• Develop CFD models based on physical principles for thermal energy storage units, including heat transfer, fluid flow, and sensible or latent heat storage alternatives.
• Model storage using liquid piston and molecular spring, coupling gas compression and expansion with water intrusion and extrusion in hydrophobic nanoporous materials, as well as pressure-volume-temperature behavior and hysteresis.
• Verify numerical models, calibrate them, and validate them with laboratory-scale and prototype experimental data, collaborating closely with experimental research staff and, where appropriate, planning and executing experimental campaigns.
• Develop and research hybrid modeling approaches based on physical principles and artificial intelligence/machine learning (AI/ML) for thermal and mechanical energy storage systems, with the aim of creating reduced-order predictive models, optimizing operating conditions, and supporting AI-assisted energy management and control strategies.
• Evaluate storage capacity, energy efficiency, thermal response, and operating ranges; optimize component designs and operating conditions.
• Develop reduced-order models when necessary and collaborate with European partners on system modeling, thermal integration, scaling, and demonstration activities.
• Document and communicate results through project reports, conferences, and scientific publications.

Area: Thermal Energy Storage and Conversion (TESC)
Group: Interfacial phenomena, colloids and porous media & Phase transitions and critical behaviors

• Flexible working hours that promote work-life balance and self-organization.
• On-site working model with the possibility of teleworking.
• A welcome program that offers:
• Support in finding accommodation
• Free language courses (Spanish, English and Basque)
• Full assistance with administrative procedures, help with schools for children....

For more information: https://cicenergigune.com/es/trabaja-con-nosotros

All interested individuals are invited to submit a detailed curriculum vitae and a cover letter through this website.

The selection process ends once the candidate has been selected.

CIC energiGUNE is committed to affirmative action, equal opportunities and diversity in its workforce. Therefore, as part of our commitment to inclusion, we accept applications regardless of gender identity, ethnic origin, sexual orientation, disability and age.

• Training in engineering, preferably mechanical engineering or a related specialty, with a degree that allows access to a doctoral program
• Solid knowledge of thermodynamics, heat transfer, and fluid mechanics, as well as high analytical, quantitative, and problem-solving skills.
• Clear motivation to pursue a PhD that combines modeling and experimental work.
• Ability to communicate scientific work in English and collaborate effectively in an interdisciplinary and international research environment.

We especially value the following knowledge and experience:

• Initial experience in numerical modeling, especially finite element analysis, computational fluid dynamics, or coupled multiphysics simulations, using ANSYS Fluent, MATLAB, Python, or similar tools. Knowledge of Dymola/Modelica will be valued.
• Experience in scientific programming, numerical methods, data analysis, or verification and validation of models against experimental data.
• Knowledge of thermal energy storage, phase change materials, heat exchangers, gas compression, porous materials, or interfacial phenomena.
• Research experience through a final degree project, internships, or laboratory work, and an interest in translating models into designs for larger-scale demonstration.
• Knowledge or experience in reduced-order modeling, surrogate models, machine learning, data-driven modeling, or optimization techniques, preferably applied to engineering or energy systems.
• Experience in control systems, digital twins, energy management systems, or model predictive control (MPC) will be valued.

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