Research Assistant in AI-assisted BESS Cooling Prediction and Digital Twin

Department: Center for Environmental Intelligence (CEI)

Location: VinUniversity, Hanoi, Vietnam

Duration: 3 months, from [08/06/2026] to [07/09/2026].

Scope of Work

The RA will support dataset preparation, cooling energy prediction, preliminary BESS system design, and digital and cognitive twin/ontology study for BESS cooling development.

Main responsibilities include:

• Generate synthetic datasets representing server load, thermal load, cooling demand, CDU/pump operation, grid electricity demand, BESS state of charge, and charging/discharging profiles.
• Develop a data-driven (AI) workflow to estimate thermal load, cooling energy demand, total system energy demand, and peak demand.
• Evaluate prediction performance using suitable performance metrics.
• Develop preliminary BESS sizing and scheduling methods considering cooling demand, grid tariff, SOC limits, charge/discharge efficiency, backup requirement, peak shaving, and energy cost reduction.
• Conduct a preliminary study on digital and cognitive twin architecture, ontology, knowledge graph, RAG, and AI-agent-based decision support for BESS cooling systems.
• Prepare figures, scripts, datasets, and technical documentation for project reporting and publication.
• Support future integration of prediction, BESS scheduling, ontology, and knowledge-based reasoning modules into the digital/cognitive twin framework.
• Maintain organized records of datasets, assumptions, models, parameters, results, code, and limitations.
• Comply with NDA, research ethics, data management requirements, and VinUniversity policies.

KPIs: By the end of the contract, the RA is expected to deliver:

• Public and synthetic dataset package for thermal load, cooling energy demand, grid operation, and BESS operation.
• Cooling energy demand prediction workflow.
• Preliminary BESS sizing, scheduling, and system design method.
• Preliminary study on cognitive twin and ontology for BESS cooling systems.
• Initial ontology and knowledge graph concept for cooling demand, BESS operation, grid interaction, and control decisions.
• Technical report on dataset preparation, prediction results, system assumptions, limitations, and next-phase development.
• Contribution to one Q1/Q2 journal paper.

Qualifications:

• Bachelor’s or Master’s degree in AI, Computer Science, Electrical/Energy, Mechanical/Thermal Engineering, or related fields.
• Strong foundation in machine learning (e.g., regression, time-series) and proficiency in Python (NumPy, Pandas, ML frameworks).
• Experience in data processing, dataset generation (including synthetic data), and model evaluation.
• Basic understanding of energy systems, BESS, or cooling/thermal systems is desirable.
• Familiarity with digital twin, ontology, knowledge graph, or AI-based decision systems is a plus.
• Good research, documentation, and English skills; ability to work independently and in teams.

What we offer:

• Competitive Salary: An attractive salary is negotiable based on experience and expertise.
• Co-authorship opportunities in publications for high-ranked journals.
• Collaboration with a diverse team of researchers, including PhDs, postdocs, and faculty members.
• Access to all facilities, including the sports complex, at VinUniversity campus.
• Opportunity to work on cutting-edge AI research projects.
• VinUniversity is an equal-opportunity employer committed to diversity and inclusion in the workplace.

How to Apply:

Please submit your CV (including education, work experience, and list of publications) and relevant documents as a single PDF file, named using the format: “Research Assistant_YourName_CV.pdf”.

Use the appropriate subject line when applying: Research Assistant – BESS Digital Twin– CEI. Kindly send the file to the CEI Officer at [email protected], and cc Assist. Prof. Ahmad Hajjar at [email protected].

Applications will be reviewed on a rolling basis until the position is filled. Only shortlisted candidates will be contacted.