We are recruiting one doctoral researcher (3 years) to the AEGIS network
AEGIS — Adaptive Grid-Interactive Edge Datacenter Fleets — is a European research and training network. It is funded by the EU through a Marie Skłodowska-Curie Doctoral Network, runs from 2026 to 2030, and trains 15 Doctoral Candidates (PhD students). The DN targets developing software-defined technologies that turn edge data centers from passive electrical loads into active, carbon-efficient and grid-interactive resources. The project has three layers:
A programmable infrastructure layer gives safe, runtime-verified control of a site’s power electronics, cooling, and AI hardware.
An intelligence layer reads real-time grid signals and turns a request such as “reduce power by 20%” into the detailed actions that hardware and facilities must take, without breaking service.
A fleet orchestration layer coordinates many sites, even when they have different owners, using trusted and privacy-preserving methods, so the whole fleet can act as one Virtual Power Plant.
Read more about the project and other participants here: https://aegis-dn.eu/
Your role:
You will be working on “End-to-End Energy Modeling for Distributed AI Services”. To make carbon-aware decisions, both orchestrators and developers need to see the real energy cost of an AI workload. In this project you build analytical models and systems that make energy and carbon metrics visible. You will create compositional energy models that span the radio network, the transport network, and the datacenter hardware, and expose them through a fast query interface so any tool can ask, “what does this AI service cost in energy?”
You will be responsible for:
Developing compositional energy models for disaggregated radio (RAN) components, transport networks, and datacenter hardware.
Building a low-latency query API that returns the end-to-end energy cost of an AI service.
What you will deliver:
Validated models with less than 10% prediction error on the Aalto and Ericsson testbeds.
A query API that returns end-to-end energy-cost estimates in under 100 milliseconds.
A doctoral dissertation that you will defend.
Secondments:
You will spend two research visits with partners in the network:
Ericsson (Finland) — 3 months from around May 2028 (industry), with Tomas Mecklin: test the energy models on Ericsson’s infrastructure testbeds.
TU Wien (Vienna, Austria) — 3 months from around May 2029 (academic), with Ivona Brandić: combine the digital-twin models with the end-to-end energy models.
The position requires:
Master's degree in computer science, communications engineering, telecommunications, or a related field (with a CGPA of at least 4/5)
Experience (e.g. coursework, projects, etc.) in AI and machine learning, including understanding of various DNN architectures, training models, and applying them for different tasks
Understanding of computer and telecom networks (for example RAN)
Experience with empirical measurements (Prior work on energy or power measurement is strongly preferred)
Data analysis and analytical modeling — identifying the features that drive system behaviour and building compositional or analytical models from measured data (an interest or aptitude here is enough; we don’t expect prior demonstrated experience)
Good programming skills – you are required to provide a sample of your code during the recruitment process
Fluency in written and spoken English. See https://www.aalto.fi/en/study-options/aalto-doctoral-programme-in-electrical-engineering#8-required-language-proficiency for information on how you can demonstrate proficiency in English. This is required when officially applying for doctoral studies at the School of Electrical Engineering in Aalto University (within 6 months after starting in the job). Please note that you do not need official proof when applying for this job.
If you are chosen for this position, you will apply for the study right in doctoral studies at Aalto University…