Decision-making under uncertainty for commodities and financial markets

We are very happy to announce the upcoming EURO Summer Institute on Decision-making under uncertainty for commodities and financial markets to be held in Ischia, Naples (Italy). The date for the institute is September 15 – 25, 2024. For more info please visit the ESI website https://esi2024.unibg.it/ 

As announced on the website, the deadline for application submission for the EURO Summer Institute on „Decision-Making Under Uncertainty for Commodities and Financial Markets“ to be held at Sorriso Thermae Resort, Forio d’Ischia (Na), Italy, September 15 – 25, 2024 has been extended to May 30, 2024.

Who can apply?

  • The applicant has to be a current PhD student or researcher with less than two years research experience since completing the PhD.
  • The applicant has to come from a EURO member society country, or study in a EURO member society country.
  • Up to two candidates can be appointed by IFORS, according to the EURO and IFORS exchange for ESWIs (see the website for additional information).
  • The applicant must not have been a laureate of a previous EURO Summer/Winter Institute.

What to submit?

  • A Curriculum Vitae, including information about education, research projects, publications, awards, and other pertinent experiences (pdf format).
  • A single-authored or co-authored paper in the field of “Optimization under Uncertainty” where the applicant is the main contributor and which has not yet been published or accepted for publication (pdf format).
  • A statement outlining the motivation for participating in the ESI (at most one page).
  • A letter of recommendation from one referee (preferably the thesis advisor or head of department).

The institute focuses on all aspects of decision making under uncertainty and applications in commodities and financial markets. Topics of interest include, but are not limited to:

  • Optimization techniques under uncertainty
  • Stochastic programming
  • Robust optimization
  • Distributionally robust optimization
  • Portfolio selections and risk management
  • Credit risk management in financial institutions
  • Machine Learning to identify risk drivers in ESG investments