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Fondazione Eni Enrico Mattei (FEEM) is looking for a qualified and motivated researcher to fill 1 position within the programme on Technologies for the Energy Transition (TET).

The program aims at studying and analyzing the enabling regulatory, market and financial conditions for the adoption and penetration of low-carbon technologies and for a successful transition towards net-zero by 2050.
The program currently has five workstreams:

i) the management and development of a structural European power market model;
ii) the development of a Meditterranean power market model;
iii) the management and development of a macroeconometric model to assess the social and economic impacts of the energy transition;
iv) the contribution to a Horizon Europe programme on open-source energy models for the African context;
v) the estimation of the levelized cost of electricity from Nuclear Fusion.

The successful candidate will lead the project on nuclear fusion.
The objective of the research project is the definition and subsequent quantification of an economic model that, by parameterizing the performance and costs of a standard industrial plant, would be able to quantify the Levelized Cost of Energy (LCOE) attributable to it. The benefits would be extremely significant in that the model would also make it possible to:

  • highlight which component of the plant’s performance is relevant to the LCOE, thus providing very useful directional information for ongoing research;
  • highlight which raw material/cost component is relevant to the LCOE, so that criticalities in the supply of materials, their transformation processes and other supply chain characteristics can be configured early on;
  • set up sensitivities on the previous two points so as to provide first quantification of the range of expected LCOE;
  • compare these LCOEs on a like-for-like basis with those associated with different green power generation technologies;
  • define a proper regulatory framework to support this nascent technology.

The duration of the contract is one-year term and full-time, with the possibility to renew it. Should the candidate be a Ph.D. student a part-time contract can be negotiated.

The following research competences and topics are foreseen in the initial phase:

  1. Nuclear technology;
  2. Technoeconomic financial modelling;
  3. Innovation dynamics;
  4. Working with big datasets.

The selected applicant will be based in Milan (Italy) and will primarily focus on research projects. The candidate will also be asked to contribute to internal activities including conferences and workshops. As part of his/her duties, the candidate will have to finalize the research efforts in scientific articles. The results of the scientific articles may then be used for dissemination in policy briefs or blog posts.

The job assignment is expected to begin in February 2024.
Gross salary is competitive and will be based on qualification and experience.

A background with solid quantitative skills, as demonstrated by the achievement of at least a M.Sc. in a subject area related to the job description is compulsory (Economics or Energy Engineering).
A Ph.D. degree is not compulsory, but it is highly valued.

The ideal candidate:

  • is proficient in power systems and industrial organization;
  • knows the principles of structural energy models;
  • is familiar with at least one of the topics of the project;
  • is able to work independently with large datasets;
  • has sufficient command of (at least one) standard statistical packages, like R, stata and Matlab;
  • can operate in an international context with English as a working language;
  • is highly committed to develop a research project.

How to apply
Applicants can submit their application and detailed curriculum vitae here. Please mention ref: TET2024_NUC_FUSION in the application.

Deadline for applications
FEEM will begin considering candidates immediately and will continue until the position is filled.

FEEM values diversity and welcomes applications from all suitably qualified candidates regardless of age, gender, race, disability, sexual orientation, religion or ethnic background.