SUPSI Open Rank Professor (Full or Associate Professor) at the Institute of Systems and Applied Electronics

SUPSI Open Rank Professor (Full or Associate Professor) at the Institute of Systems and Applied Electronics

The University of Applied Sciences and Arts of Southern Switzerland (SUPSI) invites applications for a position as SUPSI Open Rank Professor (Full or Associate Professor) at the Institute of Systems and Applied Electronics (ISEA) within the Department of Innovative Technologies (DTI), based in Lugano. The position carries an employment rate between 80% and 100%, with a starting date to be agreed upon.

Context and Objectives of the Position

The Institute of Systems and Applied Electronics (ISEA) of the Department of Innovative Technologies (DTI) at SUPSI conducts applied research, technology transfer, and education in the field of electronics, ranging from electronic design to the development of advanced embedded and cyber-physical systems. Through close collaboration with companies, public institutions, and national and international academic partners, the Institute develops technological solutions with a high application impact and contributes to innovation within the industrial sector.

As part of its strategic development, ISEA intends to strengthen and expand its scientific expertise through the appointment of a Professor capable of developing an original, distinctive, and sustainable research line, while contributing to the scientific and educational growth of the Institute. The position is intended for candidates with solid scientific experience, a strong aptitude for collaboration, and a vocation for establishing strategic partnerships.

Areas of particular interest include, but are not limited to:

• Advanced control systems, including approaches based on artificial intelligence, machine learning, and data-driven methodologies;
• Electronics, sensing, and control for robotics and autonomous systems;
• Power electronics, drives, and electrified systems;
• Intelligent energy systems and technologies supporting the energy transition;
• Edge AI and machine learning for embedded systems and resource-constrained platforms.