PhD position in Smart Sensors & Energy Efficient Machine Learning

PhD position in Smart Sensors & Energy Efficient Machine Learning

DIAMOND seeks to appoint 16 highly motivated Ph.D. candidates to a range of positions across Europe. These Doctoral Candidate (DC) positions offer an exciting opportunity to work with leading experts across Europe to develop solutions for . This position focuses on next-generation distributed intelligence and decision support for large fleets of civil infrastructure assets, including bridges, railways, and wind turbines.

Project background

The scientific aim of DIAMOND is to develop decentralized, data-driven frameworks that enable robust monitoring and condition assessment of infrastructure fleets. By combining smart sensing with distributed intelligence and advanced stochastic modelling, the project delivers new methods for scalable prediction and decision support across diverse asset types.

The DIAMOND network The DIAMOND network brings together leading academic institutions and major industrial partners from across Europe, spanning Ireland, Italy, the United Kingdom, Denmark, France, Greece, Finland, the Netherlands, Austria, Germany, and Switzerland.

Job description

In this role, you will benefit from advanced training in various European universities and industry, participate in scientific discussions within the rich context of the Marie Skłodowska-Curie Action Doctoral Network (MSCA DN) DIAMOND.

Doctoral candidate position A-DC2 will be hosted and matriculated at ETH Zurich, with planned secondments at STMicroelectronics (Itay) and the University College Dublin. The doctoral candidate will work on designing novel Smart Sensors & Energy Efficient Machine Learning on Microcontrollers.

The objectives of this thesis include :

• Design and prototype modular, low-cost sensor nodes integrating vibration, strain, and environmental sensors with MCU-class edge AI capabilities. Optimize sensing front-ends, power management, and communication interfaces for minimal standby and inference energy.
• On-chip AI and signal preprocessing : Implement on-node feature extraction (FFT, modal parameters, transmissibility metrics) and deploy quantized TinyML models co-developed with ADC1. Enable adaptive sensing and duty-cycling strategies responsive to structural dynamics.
• Energy harvesting and lifetime optimization. Integrate multi-source energy harvesting (vibration, solar, RF, etc.) and dynamic power management policies to achieve energy neutrality and autonomous operation in long-term deployments.
• Edge–cloud integration and field deployment. Deploy and validate sensor networks on representative assets (bridge testbeds, turbine rigs), interfacing seamlessly with ADC1’s graph analytics and hybrid digital twins. Quantify energy, latency, and data-rate improvements against cloud-based baselines.

This position is offered for 36 months , with the earliest starting date as agreed upon recruitment. The primary academic advisor is PD. Dr Michele Magno (ETH Zurich, D-ITET) , under close collaboration with Prof. Eleni Chatzi (ETH Zurich, D-BAUG, supervisor of A-DC1), and additional mentorship provided during secondments.

Profile

Applicants must hold a :

We expect good interpersonal skills, the ability to thrive in a diverse, multidisciplinary environment, ability to present work in international conference, as well as the willingness to spend a number of months working with project collaborators at partner universities in this project.

Eligibility Criteria : Researchers can be of any nationality. At the time of recruitment, researchers :

We offer

We offer  you the opportunity to be a part of DIAMOND, which will not only facilitate sixteen DCs in reaching a high level of technical and project-specific excellence but will also provide you with many opportunities for developing skills that are transferable to a broader landscape of opportunities. You will have the opportunity to visit industry and other academic institutions within the consortium. After completing the program, you will have a thorough understanding of the process from research via innovation to industry implementation and a strong career-defining network.

Working, teaching and research at ETH Zurich

We value diversity and sustainability

In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish. Sustainability is a core value for us – we are consistently working towards a climate-neutral future.

Curious? So are we.

We look forward to receiving your online application including :

The applications will be reviewed starting January 15th 2026 with the intent to conclude the search by April 30th 2026. Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.