A researcher in AI-Driven Energy Storage Modelling, Optimisation and Control

The Centre on Sustainable Energy (SE) supports the development of devices and methods for generation, storage, and distribution of energy solutions at low environmental impact. This will be done in the perspective of energy sustainability, of systems and solutions that respect the environment and the quality of life, of solutions with a lower impact on health and on environmental pollution levels. The ground for the Centre on “Sustainable Energy” is based on the Decarbonization targets, which will demand for more flexibility of the energy system, through new gas and power grids and using energy vectors and storage solutions, as enablers for the wide penetration of renewables.

The Battery and Electrification Technologies (BET) Unit targets the development and optimal use of battery technologies in both mobility and stationary applications. BET focuses on current and upcoming battery and storage technologies (e.g., LIBs, SSBs, RFBs, HESS) working on a wide range of topics from material development and characterisation to demonstration activities in relevant environment, including of course modelling and optimal management.

Job Description

The position concerns the modelling, optimisation, and advanced management of battery energy storage systems (BESS) and more in general energy storage systems (ESS) and their interaction with the power grid. The succesful candidate will work in the Battery and Electrification Technologies (BET) Unit, supporting both European and national projects. The position is offered within the Horizon Europe project SMART-ESS (Smart Multi-Layer AI-Based Resilient Twin for Advanced Control of Energy Storage Systems), which develops and demonstrates advanced modelling, hybrid AI control, and digital twin technologies to improve the performance, flexibility, and integration of energy storage systems into power grids.

The activities of the successful candidate will focus on:

• Developing and parametrising physics-based battery models, including electrochemical models, to represent battery performance, operating conditions, and degradation;
• Developing data-driven and hybrid AI models for battery state estimation, degradation prediction, and real-time applications;
• Developing and implementing optimisation and energy management strategies for BESS, considering operational constraints, battery degradation, grid requirements, multi-market participation and multi-service provision;
• Developing and implementing power system models and Optimal Power Flow (OPF)-based optimisation methods to assess the impact of energy storage on grid flexibility and system operation;
• Supporting the integration of battery models and optimisation algorithms into digital twin platforms and their validation through simulation and real-time testing;
• Writing technical deliverables, reports, and scientific papers.

Job requirements

The ideal candidate should have:

• MSc in Electrical Engineering, Energy Engineering, Automation and Control Engineering, Applied Mathematics, or other relevant fields;
• Solid knowledge of energy storage technologies, particularly battery energy storage systems (BESS), their operation, modelling, and integration into power systems;
• Experience in mathematical modelling and simulation of battery systems;
• Experience in optimisation techniques for energy storage scheduling, energy management, and operational planning;
• Knowledge of electricity markets, ancillary services, and the role of energy storage in providing grid flexibility;
• Knowledge of data-driven modelling, machine learning, and their applications to energy storage systems;
• Familiarity with electrical power system modelling including OPF, grid-constrained optimisation;
• Ability to analyse simulation and experimental data, identify model parameters, and validate numerical models;
• Programming skills in Python, MATLAB/Simulink, C++ or other relevant programming languages
• Ability to write technical reports and scientific papers;
• Good knowledge of written and spoken English;
• Ability to manage multiple projects effectively;
• Ability to work in a collaborative environment, with a strong commitment to achieving assigned objectives;
• Good communication and interpersonal skills;
• Self-motivation and result orientation.

Furthermore, the following elements will be positively evaluated:

• PhD degree in fields related to this call;
• Experience in AI-based and physics-informed modelling techniques, particularly Physics-Informed Neural Networks (PINNs), for energy storage applications;
• Experience with advanced optimisation, such as Model Predictive Control (MPC).
• Familiarity with digital twin technologies, real-time simulation, and energy management systems;
• Good knowledge of written and spoken Italian.

Employment

Type of contract: fixed-term contract

Working hours: Full time (38 h per week)

Start date: Preferably January 2026

Duration: 24 months with the possibility of extending the contract depending on funding

Workplace: Povo (Trento) and Rovereto

Gross annual salary: From €41.245,00 to €47.182,01 depending on background and expertise in the field, plus objectives achievements bonus

Benefits: flexi-time, company subsidized cafeteria or meal vouchers, internal car park, welcome

office support for visa formalities and for research in accommodation, accommodation etc., supplementary pension and health fund, social security (SANIFONDS), family-work balance, free training courses, support on bank account opening, discount on public transport, sport, language course fees, counseling and psychological support service. More info at https://www.fbk.eu/en/work-with-us/

Application

Interested candidates are requested to submit their application by completing the online form. Please make sure that your application contains the following attachments (in pdf format):

• Detailed CV including relevant past experiences (as an attached document in PDF format);
• Cover Letter (explaining your motivation for this specific position).
• At least 2 professional references (e-mails and/or phone numbers)

Application deadline: 01/12/2026