Ofertes d'ocupació a Trinasolar

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. Python TSQL C++ Git REST PostgreSQL Modbus MATLAB Machine Learning

Description of the contracting Organisation

The contracting organisation is Trina Hydrogen - a technology company specialising in the development and commercialisation of solutions for the energy transition, with an international presence and experience in projects relating to renewable energy, energy storage, hydrogen and new sustainable fuels. As part of its R&D strategy, the company aims to strengthen its capabilities in the digitalisation, modelling and advanced optimisation of energy facilities through the development of new in-house tools based on digital twins and artificial intelligence.

Objective of the R&D project

To develop a modular and scalable digital twin platform for the design, simulation, optimisation and technical-economic analysis of renewable hydrogen facilities and their main derivatives. The platform will be centred on hydrogen production via electrolysis and will enable the gradual incorporation of hydrogen refuelling stations, synthetic sustainable aviation fuels, green ammonia and methanol, as well as their integration with renewable generation, electricity storage and auxiliary systems.

The models will incorporate electrical operating constraints, conversion losses, equipment degradation and availability where relevant to sizing and operating decisions. Extensions of the platform will be prioritised within the three-year programme according to data availability, validation resources and technical relevance.

Academic profile

We are seeking a professional holding a PhD in one of the following areas or equivalent disciplines:

• Electrical Engineering.
• Control and automatization engineering.
• Computer Engineering with a specialisation in industry or energy.
• Applied mathematics or physics with experience in energy systems modelling.

A doctoral thesis or research experience relating to digital twins, energy system modelling, process simulation, optimisation, artificial intelligence, renewable energy, hydrogen or technologies for converting renewable electricity into fuels and chemicals will be particularly valued.

Priority technical skills

• Development of digital twins and parametric models.
• Modelling and simulation of energy systems or industrial processes.
• Mathematical optimisation and multivariable analysis.
• Artificial intelligence and machine learning applied to physical systems.
• Scientific programming and advanced data processing.
• Integration of physical models with data-driven models.
• Time series analysis and processing.
• Technical-economic modelling and scenario analysis.
• Integration of different models, databases and information sources.

Electrical integration and operational modelling

The candidate should be able to represent AC, DC and hybrid architectures at system level, including power balances, grid import limits, converter losses and equipment operating envelopes. Modelling should account for battery state of charge, power and energy limits, efficiency and ageing, together with electrolyser minimum load, ramp rates and start and stop behaviour.

Detailed expertise in load flow, voltage and reactive power behaviour, short circuit studies, power quality, electrical protection and converter control is desirable. The candidate should identify when specialist electrical studies are required and be able to incorporate their results into the energy models.

Programming languages and tools

• Python is essential: structured scientific programming with NumPy, pandas and SciPy; technical plots with Matplotlib; Jupyter for exploration alongside reusable modules and scripts.
• Optimisation in Python is essential: experience with a suitable framework such as Pyomo, CVXPY or SciPy optimisation, including formulation, solver selection, feasibility checks and sensitivity analysis.
• Applied machine learning remains a priority skill: practical experience with a suitable framework such as scikit-learn. PyTorch or TensorFlow would be valuable for applications requiring more advanced models.
• MATLAB and Simulink are strongly desirable for dynamic modelling and control studies. Experience with Simscape Electrical, PowerFactory, ETAP, PSCAD or PSS/E is desirable for specialist electrical work. Equivalent relevant simulation experience is acceptable.
• SQL is desirable for querying and combining operational and test data, for example in PostgreSQL or SQLite. C or C++ is advantageous for performance-critical routines and embedded or real-time interfaces.
• Git, environment and dependency management, documentation and meaningful automated tests, for example with pytest, are essential for reproducible and maintainable software.

Proficiency in every named package is not expected. The candidate should demonstrate a coherent set of tools appropriate to the research and explain the limitations of the selected methods.

Particularly valuable skills could be evaluated

• Integration of electrolysers with photovoltaic and wind power generation and battery storage systems.
• Process simulation and dynamic systems tools.
• Mathematical optimisation tools.
• Energy management, monitoring and control systems, sensor networks and industrial data acquisition.
• Technical and economic assessment, levelised cost of hydrogen and energy investment models.
• Condition monitoring, reliability analysis, laboratory or commissioning experience, and hardware in the loop testing.

Main responsibilities

The researcher will be responsible for defining the scientific and technological architecture of the digital twin, developing and integrating the various energy and process models, establishing input and output variables, developing simulation and optimisation algorithms, and progressively validating the models against design data and, where available, real-world data from facilities.

Development will begin with green hydrogen as the common core of the platform, creating a modular architecture that will subsequently allow for the incorporation of hydrogen refuelling stations and the main conversion pathways towards sustainable synthetic aviation fuels, green ammonia and green methanol. The ultimate aim will be to have a tool capable of comparing configurations, sizing facilities, simulating different generation and demand profiles, optimising operations, and providing technical and economic indicators to support design and decision-making in new projects.

The researcher will establish validation criteria, clean and synchronise time series, check units and sensor quality, calibrate models and distinguish model error from measurement uncertainty. Familiarity with REST APIs, CSV, JSON and industrial interfaces such as OPC UA or Modbus is desirable.

Additional responsibilities will include planning research milestones and tests, documenting assumptions and uncertainty, and using model predictions to investigate performance deviations. Validated results should inform design, commissioning, operation and maintenance recommendations.

Professional profile sought

We are seeking a researcher with a PhD, strong analytical and programming skills, the ability to work independently to develop and lead R&D activities, and a focus on transforming scientific results into tools applicable to real-world energy projects. Particular value will be placed on a combination of a solid scientific foundation, programming skills and knowledge of energy processes, rather than specialisation that is overly limited to a single technology.

Professional English is required for technical reporting and collaboration. Spanish is desirable for local site and contractor coordination. Selection should include a relevant modelling example, a Python code sample or practical exercise, and a discussion of model validation. Publications, patents, software outputs or industrial reports can provide supporting evidence.

Terms of employment

The role involves joining an R&D Department, based in Madrid, Spain. (Funding is intended to be sought under the Torres Quevedo Grants scheme and remains subject to award). Professional and remuneration conditions will be agreed according to qualifications, experience and responsibilities.