Department of Statistics and Operations Research

FPU-UPC 2026: Two predoctoral positions assigned to DEIO

Mar 09, 2026

The UPC has published the FPU-UPC 2026 Call for PRE-DOCTORAL CONTRACTS for university teaching staff training. These are 4-year contracts for pre-doctoral research staff, with a gross salary of €20,063.96 for the first two years and €25,079.88 for the last two. There will be two calls (or editions) throughout the year, one now and the other in October. Here you have the link to the call.

The Department of Statistics and Operations Research at UPC has been assigned TWO contracts this year, one in the first edition (application deadline from February 20 to March 20) and the other in the second (from October 23 to November 20).

Thesis topics must be in the field of statistics, operational research or data science. Some proposed thesis topics are the following:

  • Building Resilient Rural Futures: develop models and algorithms to strengthen rural resilience by improving inclusion, access to services, and collective decision-making under uncertainty. (Jessica Rodríguez)
  • Combined timetable tables in systems of public transport lines and rail passenger transport, with robustness in the presence of incidents. (Esteve Codina)
  • Deep learning-based heuristics for combinatorial optimization problems in data privacy. (Jordi Castro)
  • Enhancing interior point solvers for structured optimization problems. (Jordi Castro)
  • Exploring spline-based neural networks as an alternative to standard ones. (Pedro Delicado)
  • Market-clearing in nonconvex electricity markets. Advantages and challenges of convex hull pricing and alternatives. (Albert Solà)
  • Measuring dependence in Bayes Spaces (Maribel Ortego, Pedro Delicado)
  • Modeling of cooperative railway systems of freight transportation through methods in mathematical programming for their planning and operation. A case studies-based approach. (Esteve Codina)
  • Statistics and AI-based advanced methods for monitoring, diagnosing, and anticipating failures in wind turbines. (David Agís, Francesc Pozo)
  • Vortex models defined from the classical (differential) equations using cellular automata, specifically following the m:n-CAk generalization. (Pau fonseca)