PhD student in continual on-tiny-device learning for dynamic environments (F/M) 100%

Facing the challenges of our time

Help us grow and be more impactful !

Our “Integrated & Wireless Systems” Business Unit based in Neuchâtel, Switzerland, is currently looking for aPhD student in continual on-tiny-device learning for dynamic environments

Your mission

Join our innovative team of embedded experts in the role of a PhD position in continual on-tiny-device learning for dynamic environments. Collaborate with leading European universities in the context of the MSCA Doctoral Network ANT to pioneer cutting-edge technologies that will shape the future of Embedded AI on Things. Contribute to our group's overarching goal of transferring these transformative technologies to industry partners while pushing the boundaries of innovation and state-of-the-art embedded AI.

MSCA Doctoral Network ANT

ANT is a MSCA Doctoral Networks project funded by the European Union in the call HORIZON-MSCA-2023-DN-01-01. The MSCA Doctoral Networks program aims to train entrepreneurial, innovative and resilient doctoral candidates, able to face current and future challenges and to convert knowledge and ideas into products and services for economic and social benefit.

Embedded Artificial Intelligence (AI) has emerged as a transformative technology with immense potential to revolutionize various domains, spanning from robotics and healthcare to environmental monitoring and the Internet of Things. This project aims to train a network of 15 excellent Doctoral Candidates (DCs) by addressing the fundamental challenges of Embedded AI and accelerating the development of Embedded AI systems and applications through an innovative and interdisciplinary research and training program. You can find more information about the project here: https://ant-dn.eu/.

Available PhD position at CSEM

Within this project, we have one PhD position available (DC 15). The objectives include:

• To propose continual on-device learning algorithms that are able to detect distribution shifts;
• To enable on-device continual learning with regular and forward computing with a mix of innovation on self-organised neural network topologies;
• To jointly optimize embedded on-device continual learning and network topologies in evolving environments.

Expected Results: 1) New on-tiny-device continual learning algorithms; 2) Self-organizing neural topologies; 3) Deployability studies for resource-restricted devices.

The PhD candidate will be enrolled at the Doctoral School of the University of Trento (UNITN).

Two secondments are planned:

• ST Microelectronics (4 months, M16-M19): On-device continual learning for tiny devices (KPI: joint paper)
• TU Delft (4 months, M28-M31): On-device continual learning & topology optimisation for autonomous robotics with Prof. Q. Wang (KPI: joint paper)

Before applying, please check if you meet the required eligibility criteria. More information can be found here: https://ant-dn.eu/news/18-phd-positions-available-in-ant/.

Your responsibilities

• Thesis Development: collaborate closely with a partnering university to define and develop a cutting-edge thesis focused on the presented research theme.
• Innovation and Research: conduct in-depth research to advance the state-of-the-art in on-tiny-device learning for dynamic environments, exploring novel approaches and solutions.
• Industry Technology Transfer: Work on projects with the primary goal of transferring developed technologies and innovations to the industry, bridging the gap between academia and practical applications.
• Continuous Learning: stay up-to-date with the latest advancements in on-tiny-device learning for IoT, attending relevant conferences, workshops, and seminars to enhance knowledge and skills in the field.
• Publication: disseminate research findings by publishing in top-tier academic journals and presenting work at prestigious conferences, contributing to the field's knowledge and recognition.

Your profile

Know-how

• MSc degree in EE (electronics) or CS (computer science): possess a foundational understanding of signal processing, artificial intelligence and resource-constrained optimization with the potential to learn and apply these concepts effectively.
• Familiarity with embedded systems and Edge AI (experience with platforms like ESP32, Cortex-M MCUs, NPUs, etc.).
• Programming Skills: proficiency in programming languages like C, C++ and Python for code development and ML model training and inference.
• Academic Excellence: strong academic record, with a history of outstanding performance in relevant coursework.

Interpersonal skills

• Natural curiosity and ability to adapt to new situations
• Autonomous and hands-on, motivated in playing a key role in the development of innovative solutions
• Open-minded attitude and well-developed team-spirit
• Excellent communication skills, both written and verbal.

CSEM mission and values

Our mission is the development and transfer of innovative technologies to the Swiss industry. Our objective is to make an impact on our customers and on society at large in the fields of precision manufacturing, digital technologies and sustainable energy. Our strength is the excellence of our people, about 600 passionate specialists dedicated to innovation and technology transfer. We believe that strong values support the successful development of our organization as well as the harmonious and balanced development of all our employees.

We are

• A unique place between research and industry at the cutting edge of new technologies
• An innovative, non-profit, and employee-driven company
• A dynamic, multidisciplinary, and multicultural environment
• A solar team focused on enabling solutions to energy challenges for a sustainable world

Working@CSEM means

• being part of a passionate community
• incredible flexibility, attractive working conditions, and great opportunities of development
• benefit from a management style based on trust & feedback and that favors a work-life balance

We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity.

We look forward to receiving your complete application file via (CV, cover letter, certificates & diplomas) our job page.

Preference will be given to professionals applying directly.