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Transparent optimization models for better energy policy

Would you like to help companies and institutions make better decisions, in the field of energy planning and potentially across industries? Would you like to develop new techniques that will build bridges between mathematic results and actionable understanding? In this project, you will develop your research in the frontiers of Optimization and Machine Learning, creating new techniques that will improve decision processes within institutions and the use of models in policy making. The project will focus on explaining solutions in terms that can be understood by the humans responsible of the decision. The research work will require a theoretical component and a practical one, where the candidate will have the opportunity to get to know the power secto. He/She will be integrated into a n interdisciplinary team where he/she will grow in experience and knowledge in a fast-growing research field that is expected to grow in prominence in the next few years.


MSc  Basic knowledge of Optimization (LP, MIP) and Machine Learning (scikit-learn). Preferably an engineer or mathematician.
Advanced English level, and very good academic record. Experience in model development (GAMS or Pyomo). Good writing skills. Resilience. A taste for discovery.

What do we offer?

Annual full-time contract for the development of research project, which totals 3 years with the possibility of extending to a 4th one. In addition, training activities may be carried out, with IIT covering 90% of the cost. If the candidate has a MSc, he/she will enrol in the PhD program at Comillas and is expected to get a PhD in the duration of the project. The gross salary is € 25896,34. In addition, the candidate would enjoy all the advantages offered to Comillas students (Sports, Cultural Activities, Library, etc.). In addition, the program has an international vocation, and the candidate will have the possibility of undertaking a research stay abroad.


Curriculum Vitae, academic record, cover letter and two recommendation letters