Rebound parameterisation for consequential LCA decision support in imec.netzero

Context

New GPUgenerations and other efficiency improvements can reduce the energy requiredper AI inference. However, in practice, lower cost and higher performance canalso increase demand (more utilisation, faster adoption, shorter replacementcycles, or new use cases). This demand response - often referred to as rebound- can partly or fully offset the expected environmental savings. Within theSSTS program, imec.netzero is developing a decision-support layer based onconsequential life cycle assessment (CLCA) to make these trade-offs explicit.

This work supports real-world decisions in imec.netzero by makingit possible to test "what-if" scenarios (e.g., GPU upgrades) with a consistent,documented representation of demand response, so expected savings are notoverstated.

Objective

The objectiveof this internship is to build a practical, transparent rebound representationthat can be used in imec.netzero CLCA studies. This will be achieved by:

• Reviewing and synthesising literature on rebound effects relevantto digital services, ICT, and efficiency-driven technology transitions.
• Defining a small set of reusable rebound channels (e.g., increasedservice demand, higher intensity/quality, faster adoption, shorter replacementcycles, and constraint effects).
• Translating the evidence into a compact parameter library (cleardefinitions, units, plausible ranges, and uncertainty notes).
• Packaging these parameters into a set of standard demand-responsescenarios (e.g., fixed / low / medium / high) that can be directly used in CLCAcalculations.

Responsibilities

You willactively engage in the evidence gathering and modelling of rebound scenarios,and in preparing implementation-ready inputs for the imec.netzero CLCA decisionlayer. This will involve working closely with the sustainability research teamto ensure correct interpretation of CLCA concepts, as well as with thedevelopers to ensure the outputs can be integrated smoothly.

In practice,this will involve:

You will turn literature findings into a smallset of parameters and scenarios that can be used directly in CLCA runs.

• Extracting quantitative values and definitions from key sourcesand logging them in a structured template.
• Harmonising assumptions (scope, boundaries, units) so parametersare comparable and reusable.
• Drafting short, clear documentation explaining what each parametermeans and when it applies.
• Testing the scenario set on a simple illustrative case (e.g., aGPU upgrade example) to confirm the workflow is usable.

Skillsand Learning Objectives:

Applicantsare expected to have a general background in engineering, environmentalassessment, data science, or a related field. Experience with structured datahandling (Excel/CSV) is required; Python is a strong advantage. During theinternship, you will gain proficiency and enhance your skills in the followingkey areas:

• Consequential LCA and decision-support modelling
• Rebound effects and scenario design under uncertainty
• Transparent parametrisation and documentationpractices
• Working withresearchers and developers to translate concepts into tool-ready inputs

Type of internship: Master internship, PhD internship

Required educational background: Energy, Materials Engineering, Other, Finance, Electrotechnics/Electrical Engineering

Supervising scientist(s): For further information or for application, please contact Hanie Zarafshani ([email protected]) and Job Soethoudt ([email protected])

The reference code for this position is 2026-INT-047. Mention this reference code in your application.

Imec allowance will be provided.

Applications should include the following information:

• resume
• motivation
• current study

Incomplete applications will not be considered.