PhD Students in Neuromorphic Nanorobotics Based on Artificial

The University of Applied Sciences and Arts of Southern Switzerland (SUPSI) is offering two PhD positions in Artificial Intelligence-Based Neuromorphic Nanorobotics at the Dalle Molle Institute for Artificial Intelligence Studies (IDSIA USI-SUPSI) . Full-time employment is expected.

Starting date: November 1, 2025, or to be agreed upon.

Candidates will be enrolled in the doctoral program at the Institute of Neuroinformatics at UZH-ETH Zurich.

Responsibilities and activities Research activities in the fields of artificial intelligence, i.e., spiking and artificial neural networks, ultra-low-power neuromorphic embedded systems, and robotics on nano-sized platforms.

Candidates will have the opportunity to participate in an engaging and stimulating international research project, funded by the Swiss National Science Foundation (SNSF), focused on innovative technologies for the development of a new generation of highly intelligent nanorobotic platforms (i.e., less than ten centimeters in diameter and weighing a few tens of grams), capable of autonomous driving, and characterized by novel aspects of neuromorphic perception.

Develops hybrid deep learning algorithms, both video-frame-based and event-based (i.e., event-based camera), for visual inertial odometry tasks.

Program ultra-low-power embedded platforms such as ARM and RISC-V microcontrollers.

Expertise in studying the state of the art in the fields of visual inertial measurement systems, both with traditional geometric approaches and based on deep learning.

Implements and optimizes algorithms for embedded systems with limited hardware capabilities – memory and computing power.

Writing scientific publications (e.g., both conference papers and scientific journals) and internal reporting.

Presentations of your work to a large audience (e.g., conferences, project meetings). University teaching assistance, both at SUPSI and at UZH-ETH Zurich.

Requirements Essentials Master's degree in electrical engineering, computer science, or related fields.

Candidate 1: A solid and relevant background in embedded systems and electronics engineering, with a good foundation in machine/deep learning.

We are seeking a highly motivated candidate who is eager to expand and enrich their technical/scientific knowledge, both theoretically and appliedly, particularly in machine/deep learning.

Candidate 2: Excellent background in machine/deep learning and solid basic experience in embedded systems and electronics engineering.

The candidate must demonstrate a strong interest in personal growth in both fields, particularly embedded systems, thus enriching their technical/scientific knowledge, both theoretically and appliedly.

Highly motivated, with a strong passion for scientific research, and a desire to share their work through high-profile international publications at both conferences and in scientific journals.

Excellent theoretical background in computer architectures or embedded systems (low-power and ultra-low-power digital signal processing). Excellent theoretical and practical background in tiny machines (TinyML), deep learning, and computer vision for robotics applications.

Experience in C programming for embedded devices and microcontrollers (e.g., STM32, ESP32, GAP8/9). Experience in Python programming and familiarity with development frameworks such as PyTorch, Tensorflow, and Tensorflow Lite.

Solid theoretical knowledge of visual inertial odometry algorithms for robotics, both traditional geometric and deep learning-based approaches.

Familiarity with perception and neuromorphic processing, such as event cameras (perception) and spiking neural networks (processing). Proficiency and fluency in written and spoken English.

Good organizational skills and the ability to carry out work in a planned and independent manner.

Strong motivation and dedication to scientific research and meeting deadlines.

Interest in teaching and supervision of thesis students.

Preferable Experience with low-level C/assembly optimizations.

Experience in analyzing digital signals and using associated laboratory instruments (oscilloscope). Familiarity with parallel computing (e.g., CUDA) on many-core, multi-core architectures, and heterogeneous systems (e.g., NVIDIA Tegra, ARM big.LITTLE). Familiarity with robotic platforms, such as Bitcraze, Crazyflie, and nano-drone.

Familiarity with low-level drivers for sensors (e.g., IMU) and their communication protocols (e.g., I2C, SPI). Demonstrable practical experience in applied projects, such as competitions, hackathons, etc.

SUPSI offers SUPSI offers a stimulating environment and unique opportunities for those who wish to grow professionally and actively contribute to innovation.

Specifically, the position offers: A fixed-term contract with the possibility of renewal.

Enrollment in the doctoral program at the Institute of Neuroinformatics at the University of Zurich (UZH) and the Swiss Federal Institute of Technology (ETH Zurich) – The doctoral degree will be awarded by both UZH and ETH Zurich.

An international research environment, with high-level academic and industrial collaborations.

Participation in high-profile conferences and workshops, with opportunities for scientific and professional networking.

Remuneration in line with the standards of the Swiss National Science Foundation.

Reimbursement of costs for participation in high-profile conferences, workshops, and schools.

Additional information Further information can be requested from Daniele Palossi and Prof.

Dr.

Melika Payvand (). Applicants must submit the following documentation, written in English: Curriculum vitae with a list of publications (if any, including those submitted) and a link to your Master's thesis (including a draft if graduation is scheduled for Fall 2025). Cover letter including a brief description of your experience (1-2 pages). List of two references (name and email address). Only applications submitted by September 30, 2025 using the appropriate form will be considered . Incomplete applications, applications sent to other addresses, or applications submitted after the deadline will not be accepted.