The Human Resources Strategy for Researchers
Marie Skłodowska-Curie Actions

PhD position 02 – MSCA COFUND, AI4theSciences (PSL, France) - “AI-supported optimisation of multi-actor energy systems enhanced with privacy & confidentiality preserving data sharing”

This job offer has expired

    Université PSL
    MathematicsApplied mathematics
    First Stage Researcher (R1)
    26/02/2021 23:00 - Europe/Brussels
    France › Sophia Antipolis
    H2020 / Marie Skłodowska-Curie Actions COFUND


“Artificial intelligence for the Sciences” (AI4theSciences) is an innovative, interdisciplinary and intersectoral PhD programme, led by Université Paris Sciences et Lettres (PSL) and co-funded by the European Commission. Supported by the European innovation and research programme Horizon 2020-Marie Sklodowska-Curie Actions, AI4theSciences is uniquely shaped to train a new generation of researchers at the highest academic level in their main discipline (Physics, Engineering, Biology, Human and Social Sciences) and master the latest technologies in Artificial Intelligence and Machine Learning which apply in their own field.

26 doctoral students will join the PSL university's doctoral schools in 2 academic cohorts to carry out work on subjects suggested and defined by PSL's scientific community. The 2020 call will offer up to 15 PhD positions on 24 PhD research projects. The candidates will be recruited through HR processes of high standard, based on transparency, equal opportunities and excellence.


Description of the PhD subject: “AI-supported optimisation of multi-actor energy systems enhanced with privacy & confidentiality preserving data sharing”


Context – Motivation

The energy transition over the next decades is characterised by the development of clean energy sources like renewables (RES), which drive the transition from the centralised energy system towards a decentralised one, where new paradigms for the organisation of actors emerge. As an example, we can mention the paradigms of energy communities, industrial basins with interacting industries, microgrids, territories with sector coupling (i.e. electricity, gas, e-mobility, district heating). These paradigms are characterised by a collaborative behaviour of the actors and are based on the assumption that exchange of information may take place,while decisions are distributed at several levels that can go from the devices (i.e. agents for the management of batteries), up to aggregations at a higher level (i.e. optimisation of smart-homes, fleets of electric vehicles, microgrids). The evolution towards such paradigms is enabled through the digitalisation of the energy sector that permits higher observability and extended control and management possibilities of the assets and, consequently, of the energy systems. Today there are on-going efforts to demonstrate in practice the benefits from such new paradigms i.e. through demonstration projects. However, in real-world applications, and when it comes to industrial deployment, important gaps and bottlenecks persist. The emergence of collaborative behaviours between the involved actors in energy systems is often hindered by the constraints on strategic data exchange that naturally are imposed by regulation or commercial/industrial security constraints. Data come often from smart meters, and is thus binded by privacy and confidentiality constraints, or it comes from RES or industrial plants and is then commercially sensitive and the corresponding actors are reluctant to share. The obligation to release information as open data in different sectors does not solve the problem; often open data are of low value for operational purposes because are released as aggregated to mask confidentiality and privacy. These constraints limit the value we can extract from the available data. Without data sharing, the optimisation of the above-mentioned energy systems leads to suboptimal solutions. Research works have shown that there is a clear benefit when we assume that data of the various actors can be shared among them. For example, when forecasting the power output of a wind farm, accuracy in the next 6 hours can be improved by up to 20% if data from neighbour wind farms are used as input.

On the other hand, to operate distributed energy systems, it is often needed to implement tools that enable decision making at local level (optimisation of operation and scheduling of devices, smart-home, feeder, microgrid...). These tools are based on complex model chains that can start from forecasting of different quantities, and contain models for the optimisation ofthe various decisions (energy trading by aggregators, asset management, microgrids management, optimisation of industrial processes in an industrial basin etc.). Artificial intelligence techniques can be applied in these model chains for decision making. A major challenge is however how to develop solutions that are replicable to the potential high number of cases, robust enough to handle data that are not always available, scalable, able to comply with privacy and confidentiality constraints in the data used and capable to deal with the multiple uncertainties involved. This PhD project aims to answer these questions through a multidisciplinary research approach.


Scientific objectives, methodology & expected results

The development of distributed optimization methods, automated transactive peer-to-peer and privacy preserving algorithms offer technical solutions in the field of artificial intelligence that can resolve the above-mentioned bottlenecks and promote collaborative strategies towards common goals like the reduction of the global carbon footprint in highly integrated basins.

The overarching objective of this thesis is double fold:

  • to develop methods that permit large/big data processing, while respecting privacy and confidentiality constraints.
  •  to develop AI-based agents that are able to support decision-making in multi-actor and collaborative energy schemes and are compatible with privacy and confidentiality preserving data sharing.

Given the direct collaboration with an industrial actor, priority will be given in the use case of an industrial basin. Examples of industrial basins in Europe exist where a single basin represents up to 20% of the country's CO2 emissions. In the process of decarbonisation of the involved industries, data sharing can be critical. Key themes and questions proposed in this topic are along the following lines:

  • How can automated negotiation technology provided by peer to peer exchange design can support coordination between industrial agents in a basin?
  • Can decomposition methods be used to coordinate heterogeneous optimization problems to reach a common goal without sharing of the underlying data and models?
  • How can privacy preserving learning provide a collaborative modelling framework for the actors of an industrial basin to improve the global modelling accuracy with no or limited data sharing?

Although the applicative field in this PhD project is energy systems, the techniques developed have a replication potential to other fields like health or other. The expected results contain:

  • Algorithms for data sharing, while preserving confidentiality and privacy constraints
  • Distributed optimisation methods that implement data sharing and AI techniques
  • Evaluation on real data with focus to use cases of industrial basins and energy communities
  • Recommendations to regulatory authorities and policy makers

The proposed topic is interdisciplinary because it combines research in the field of energy systems, artificial intelligence, data science, optimisation with focus on transactive optimisation schemes and considers concept from cryptology. The international dimension is strong since it gathers Mines ParisTech-PSL, the Danish Technical University and Air-Liquide, which is a multinational company. A collaboration is established with Imperial College in the UK, which will host the PhD candidate for an internship.


International mobility

The candidate will pass a period of at least one year at DTU (Kongens Lyngby, Denmark). This will permit, among others, to apply for a European Doctorate Label. An internship is also foreseen at Imperial College in the UK. Additional stays are envisaged to laboratories with related activity to the topic like INESC TEC. In addition, short non-academic stays are foreseen at the R&D centre of Air-Liquide in Paris area and at business units in different countries with relevant activity to the PhD topic.


Thesis supervision

George Kariniotakis and Pierre Pinson



Created in 2012, Université PSL is aiming at developing interdisciplinary training programmes and science projects of excellence within its members. Its 140 laboratories and 2,900 researchers carry out high-level disciplinary research, both fundamental and applied, fostering a strong interdisciplinary approach. The scope of Université PSL covers all areas of knowledge and creation (Sciences, Humanities and Social Science, Engineering, the Arts). Its eleven component schools gather 17,000 students and have won more than 200 ERC. PSL has been ranked 36th in the 2020 Shanghai ranking (ARWU).


More Information


  • Opportunity to conduct academic research in a top 100 university in the world.
  • High-quality doctoral training rewarded by a PhD degree, prepared within Mines Paris - PSL and delivered by PSL.
  • Access to cutting-edge infrastructures for research & innovation.
  • Appointment for a period of 36 months (job contract delivered by the involved component school of PSL) based on a salary of 3100 € gross employer (including employer tax) per month or approximately a 2228 € gross salary per month.
  • Job contract under the French labour legislation in force, respecting health and safety, and social security: 35 hours per week contract, 25 days of annual leave per year (“congés annuels”). Eventual complementary activities may be accepted or proposed by the co-supervisors (maximum of 64h/year for teaching, 32 day/year for specific missions).
  • Short stay(s) or secondment in France or abroad are expected.
  • An international environment supported by the adherence to the European Charter & Code.
  • Access to AI training package, with a strong interdisciplinary focus, together with a Career development Plan.

Eligibility criteria

  • Applicants must have a Master’s degree (or be in the process of obtaining one) or have a University degree equivalent to a European Master’s (5-year duration) to be eligible at the time of the deadline of the relative call.
  • There is no nationality or age criteria, but applicants must not have resided or carried out their main activity (work, studies, etc.) in France for more than 12 months in the 3 years immediately before the deadline of the call (MSCA Mobility rule).
  • Applicants must declare to be available to start the programme on schedule.

For submitting your online application, go to: https://www.psl.eu/recherche/grands-projets-de-recherche/projets-europee...


The online application should contain the following documents:

  • English translated transcripts from the Master’s degree (or equivalent 5-year degree). A copy of the Master’s degree or a certificate of achievement will be required later on for the final registration.
  • International curriculum vitae and a cover letter explaining the reasons that lead him/her to prepare a PhD, why he/she applies to this offer and his/her professional project (guidelines will be given to the applicants in order to help him/her in the writing of his/her letter).
  • Two academic reference letters.
  • A statement duly signed on the mobility rules, availability, and conflicts of interest.


The applicants can only apply to one PhD project among the available ones. Multiple applications of one candidate will automatically make all his/her applications ineligible.

Selection process

The applications will be analysed by the Management Team for eligibility and completeness. Afterwards, the applications will be reviewed by the Selection Committee. In the pre-selection round (March-April 2021), applicants will be rated using a scoring system based on 3 criteria (academic excellence, experience, motivation, and qualities). A shortlist of qualified applicants will be interviewed during the selection round (June 2021) to further assess their qualifications and skills according to the predefined selection criteria.

All information regarding the applications (criteria, composition of the Selection Committee, requirements) can be found on the website of the programme, in greater detail.


The selection and recruitment processes of the PhD student will be in accordance with the European Charter for Researchers and Code of Conduct of the Recruitment of Researchers. The recruitment process will be open, transparent, impartial, equitable, and merit based. There will be no discrimination based on race, gender, sexual orientation, religion of belief, disability, or age.

Additional comments

The Centre for Processes, Renewable Energies and Energy Systems (PERSEE) is one of the 18 R&D centers of Mines Paris - PSL. It has a long experience since the early 90’s on problems related to renewables integration and management and planning of Energy Systems. The PhD director has published as early as in 1996 the first journal paper ever with an application of AI in the renewable energies field. Today the research in PERSEE in the area focuses on problems that involve large/big amounts of data and develops solutions based on AI for the management and planning of power systems. Research in PERSEE spans the whole range of TRLs, from the development of concepts up to their demonstration to real world environments. 


Mines Paris - PSL is a graduate school (Grande Ecole) in Science, Engineering and Economics under the tutelage of the Ministry of Industry. It provides multidisciplinary education (relying upon around 230 academics) to train high level engineers (150/year in “Master’s degree in science and executive education” track and 200 Advanced Master’s degrees/year) expected to fulfil high responsibilities in government bodies and in the industry in France and abroad. This mission is connected to a very strong research activity closely linked to the industry, that has proved its ability to innovate, anticipate and contribute to the evolutions of the socio-economic world in the fields of risk management, public policies, economics et sustainable development among others.

This long-standing link with the industry has lead Mines Paris - PSL to set up several major collaborations (Research and Education Chairs, Framework agreements…) with leading industrial partners such as SAFRAN, EDF, ArcelorMittal or Total to name a few.


On the academic side, it is a full member of Université PSL and as such participates to a wide number of structuring education and research (both applied and more fundamental) projects, especially with two other Grandes Ecoles (Chimie Paris - PSL and ESPCI - PSL). Mines Paris - PSL is also leader or acting member of several major international academic research programs and projects on its own.

Web site for additional job details

Required Research Experiences

    1 - 4

Offer Requirements

    Mathematics: Master Degree or equivalent
    ENGLISH: Excellent


  • Typical profile: Engineer and/or Master of Science.
  • Good level of general and scientific culture.
  • Good level of knowledge of English and eventually French.
  • Good analytical, synthesis, innovation and communication skills.
  • Qualities of adaptability, creativity and motivation to work in a team.
  • Coherent professional project.

Specific Requirements

  • Specific skills for this thesis: strong background in Applied Mathematics (i.e. optimization) and/or AI.
  • Skills in computer programming (i.e.PYTHON, R) are required.

Work location(s)
1 position(s) available at
Centre PERSEE, Mines Paris - PSL
Sophia Antipolis
1, rue Claude Daunesse

EURAXESS offer ID: 578921


The responsibility for the jobs published on this website, including the job description, lies entirely with the publishing institutions. The application is handled uniquely by the employer, who is also fully responsible for the recruitment and selection processes.


Please contact support@euraxess.org if you wish to download all jobs in XML.