23/02/2026

ECOBAS Winter School 2026: Generative AI for Economics and Social Sciences

Do you want to know how to build verifiable, reproducible workflows that keep you in control of assumptions, sources, and correctness—so you can move faster without lowering standards in your reseach?

With Aleš Maršál, Expert Researcher in Generative AI

Vigo and online | February 23-24, 2026 

⬇️ DOWNLOAD COURSE BROCHURE (PDF)

👉 APPLY HERE FOR REGISTRATION

AUDIENCE: ECOBAS members, including predoc and posdoc researchers under the supervision of a PI from ECOBAS; PhD students of the Inter-university Doctoral Programme in Economic Analysis and Business Strategy.

Participants will be able to:

  • Build basic understanding of why LLM work
  • Select the right model type (general vs “reasoning”; text vs multimodal) and set expectations for reliability.
  • Use prompt frameworks that scale to real research tasks (structured outputs, few-shot, critique loops).
  • Convert messy text/documents into structured data (extraction, classification, coding) with audit trails.
  • Use LLMs with tools for data cleaning, analysis, visualization, and replication while keeping outputs reproducible.
  • Apply grounding methods (retrieval/RAG concepts) and verification habits to reduce hallucinations.
  • Use vibe coding to build API gates to fetch data, clean data and conduct econometric analysis and interpret results.

More info:

AUDIENCE: ECOBAS members, including predoc and posdoc researchers under the supervision of a PI from ECOBAS; PhD students of the Inter-university Doctoral Programme in Economic Analysis and Business Strategy.

Participants will be able to:

  • Build basic understanding of why LLM work
  • Select the right model type (general vs “reasoning”; text vs multimodal) and set expectations for reliability.
  • Use prompt frameworks that scale to real research tasks (structured outputs, few-shot, critique loops).
  • Convert messy text/documents into structured data (extraction, classification, coding) with audit trails.
  • Use LLMs with tools for data cleaning, analysis, visualization, and replication while keeping outputs reproducible.
  • Apply grounding methods (retrieval/RAG concepts) and verification habits to reduce hallucinations.
  • Use vibe coding to build API gates to fetch data, clean data and conduct econometric analysis and interpret results.

More info:

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