Smart Energy Algorithms for ThermalBox® Operations

Build to Zero SL is a Spanish deep-tech company based in Seville, driving the industrial transition toward net-zero emissions through its flagship innovation, ThermalBox® - a breakthrough Power-to-Heat and Thermal Energy Storage (TES) system.

ThermalBox® enables industries to replace fossil-fuel boilers with renewable-powered heat generation, using molten salts to store and deliver continuous, decarbonized steam up to 20 barg. Designed with a modular and standardized architecture, it provides flexibility, high efficiency (97%), and reliability (98%), while integrating seamlessly with industrial processes in the chemical, food & beverage, and paper sectors.

Beyond clean heat production, ThermalBox® introduces a new paradigm for grid-interactive energy systems by offering demand flexibility services - allowing industries to optimize energy costs and participate in balancing markets. Supported by advanced cloud-based AI optimization software, it autonomously dispatches operations to minimize electricity costs and maximize renewable integration.

Build to Zero combines over two decades of expertise in renewable energy, thermal storage, and process design, with a team that has contributed to over 60% of global TES capacity. The company collaborates with leading industrial groups (such as BASF, CEPSA, and Lucart), utilities, and research institutions (including the University of Seville, DLR, and Fraunhofer) to continuously advance the performance and scalability of ThermalBox®.

Recognized as one of Europe's most promising clean-tech innovators, Build to Zero's mission is to decarbonize industrial heat and enable large-scale integration of renewable energy through smart, electrified, and flexible storage solutions - paving the way for a resilient, zero-emission industrial future.

Objective

To apply data science and cloud computing methodologies for developing optimization algorithms supporting the operation and scalability of ETESAI®, an AI-powered energy thermal storage and control system.

Description

The VISITING TALENT will join Build to Zero's R&D Data Team and contribute to the development of algorithms and cloud tools that optimize ETESAI®'s operational efficiency. The internship bridges academic research and industrial application, allowing the trainee to apply and expand their skills in coding, AI modeling, and process automation while contributing to the company's goal of decarbonizing industrial thermal energy systems.

Main Activities

• Coding and testing algorithms for predictive demand forecasting and process automation.
• Managing datasets and cloud environments (Google Cloud, BigQuery, Vertex AI).
• Participating in sprints and technical reviews with the Build to Zero R&D team.
• Documenting findings and drafting internal reports on applied data optimization methods.
• Supporting deployment of models into production workflows.

Skills Required

• Experience in coding (Python, SQL, or similar).
• Understanding of data modeling, statistics, or applied machine learning.
• Motivation to learn and operate autonomously in a startup-like, innovation-driven environment.
• Interest in renewable energy systems or sustainable technology.

Expected Outcomes

• Working prototype or demonstrator of an optimization algorithm integrated with ETESAI®.
• Practical understanding of innovation processes within a technology-based SME.
• Contribution to ongoing R&D that could support future patentable solutions or scientific publications.

Degree Level

• Master's or PhD student (or equivalent research stage) in one of the following areas:
  • Data Science, Computer Engineering, Energy Systems Engineering, Applied Mathematics, Physics, or related STEM field.
• Alternatively, demonstrable equivalent experience (e.g., self-taught or industry-trained) in programming and applied analytics relevant to energy systems.

Specific Technical Skills

• Programming: Proficiency in Python (preferred), with experience using libraries such as Pandas, NumPy, Scikit-learn, or TensorFlow/PyTorch.
• Data & Cloud Tools: Familiarity with Google Cloud Platform (GCP) services such as BigQuery, Cloud Storage, Vertex AI, and Compute Engine.
• Modeling & Optimization:
  • Understanding of time-series forecasting, optimization algorithms, and control systems.
  • Knowledge of applied machine learning or simulation modeling for energy or industrial systems is an asset.
• Version Control & Collaboration: Experience with Git/GitHub or other collaborative development tools.
• Analytical & Documentation Tools: Competence in data visualization (e.g., Matplotlib, Plotly) and clear technical reporting.

Soft Skills

• Problem-solving mindset - able to work autonomously and propose creative technical solutions.
• Analytical thinking - capacity to translate complex datasets into actionable insights.
• Adaptability and curiosity - eagerness to learn and apply new methods across disciplines (data, energy, AI).
• Communication skills - ability to present technical findings clearly in English to a multidisciplinary team.
• Collaboration & Initiative - proactive participation in team meetings and R&D discussions within a start-up environment.

Interpersonal & Organizational Skills

• Strong sense of ownership and responsibility for assigned tasks and deliverables.
• Organized and structured approach to research, documentation, and version control.
• Respect for confidentiality and intellectual property requirements in R&D projects.

Preferred Qualifications (Optional)

• Experience or research background in energy systems optimization, thermal storage, or process automation.
• Familiarity with industrial communication protocols (e.g., Modbus, OPC UA) or IoT data pipelines.
• Basic understanding of energy markets and renewable integration strategies.

The duration will be of 6 months