Student Assistant at KOF Swiss Economic Institute

Student Assistant at KOF Swiss Economic Institute

The KOF Swiss Economic Institute at ETH Zurich is the leading institute for applied research in economics in Switzerland.

KOF has a strong collaborative environment that promotes exchange between research and administrative staff across different levels and functions and is committed to rigorous, applied research on policy‑relevant topics with a connection to Switzerland.

The Research Division Innovation Economics is seeking a student research assistant to support the project in Supply Chain mapping described below. The position is part of the SWEET: RECIPE project, which examines the resilience of the Swiss energy system. We are looking for support from April 2026 through Fall 2026.

Supply Chain Mapping for the (Swiss) electricity grid components

Supply chain disruptions pose systemic risks to critical infrastructure when they delay or prevent the maintenance, repair, or replacement of essential components. Such risks are particularly pronounced when complex intermediate goods or critical raw materials are sourced from a small number of firms or countries. Vulnerabilities are amplified when dependencies occur in higher tiers of global value chains and remain opaque to operators and regulators.
This project identifies and analyzes hidden supply chain dependencies in critical components of the Swiss electricity grid. The objective is to assess Switzerland’s exposure to external production shocks that could compromise grid reliability and energy security.
We proceed in two steps. First, we use AI‑based document analysis and engineering data to construct detailed bills of materials (BOMs) for selected critical grid components (e.g., transformers, switch gear, control systems). Second, building on firm‑level product classification methods developed at KOF, we infer firms’ product portfolios from website content to identify globally which firms manufacture the relevant intermediate inputs.
By linking component‑level input requirements to firm‑level production capabilities worldwide, we are able to map concentration risks across firms and countries. The resulting analysis reveals potential bottlenecks, single points of failure, and geographically concentrated dependencies that may not be directly visible in conventional trade statistics.
Our methodology is designed to be highly scalable and could be extended to other domains. The project provides a data‑driven foundation for assessing supply chain resilience in the Swiss electricity system and supports policymakers in designing targeted mitigation strategies.

We are looking for individuals with a strong interest in research and a background in Economics, Engineering, Data science, Supply Chain Management, or a related field. Prior experience in handling and analyzing data is an asset, and a strong motivation to further develop these skills is expected. The working language of the project is English.

We need your support with tasks like the ones below:

• Assist in AI‑supported generation & validation of BOMs
• Find and collect potential data sources (industry reports, technical descriptions, ...)
• Extract firm information from business databases
• Produce descriptive statistics of the results

• Be proficient in Python
• Be available for ca. 6 months (minimum)
• You must be enrolled at a Swiss University and willing to work between 1 to 2 days per week (15h)
• At KOF, working time is typically handled very flexibly such that you can take time to study for exams or fulfill other obligations for your studies while coordinating with the research staff at KOF when tasks need to be completed

The work location is at the KOF Swiss Economic Institute, ETH Zurich, in Zurich, Switzerland. The position may require occasional on‑site visits to laboratories or field sites as needed.

• Your job with impact:
  Become part of ETH Zurich, which not only supports your professional development but also actively contributes to positive change in society.
• Direct involvement in AI‑driven research:
  Gain hands‑on experience applying state‑of‑the‑art AI methods in an academic research project with real‑world policy…