Postdoctoral Researcher in Resource-constrained NanoRobotics

Description

The University of Applied Sciences and Arts of Southern Switzerland (SUPSI) has opened one full time (100%) position for a Postdoctoral Researcher in Resource-constrained NanoRobotics, at the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI) within the Department of Innovative Technologies (DTI), located in Lugano. The degree of employment is 100%, with a contract starting on 01.07.2026, or on a mutually agreed date.

Responsibilities

• Lead and contribute to research activities in the area of energy-efficient deep learning-based miniaturized nano-robotic platforms.
• Address key challenges for the development and deployment of Tiny Machine Learning (TinyML) models on ultra-low-power (ULP) Microcontroller Units (MCUs) and System-on-Chips (SoCs). Including hardware-aware model compression methods, quantization, pruning, and knowledge distillation, as well as deployment pipeline optimization through kernel-level optimization, computational graph optimization, and tiling strategies, targeting heterogeneous ULP MCUs/SoCs.
• Contribute to the NanoRobotics Research Group’s growth by securing research funding: from the state-of-the-art surveying, to the definition of novel research ideas and proposal writing for the Swiss National Science Foundation (SNFS) and European (EU) community funding schemes (not limited to).
• Supervision of Ph. D. students working on the Nanorobotics domain.
• Lead the writing process of top-quality scientific papers, i.e., conference and journal articles, internal reports, and project reports.
• Efficient and effective teamworking with colleagues, collaborators, and digital hardware design experts.
• Presenting your work to a wide audience at conferences, project partner review meetings, seminars, workshops (not limited to).
• Teaching activities at SUPSI and supervision of Bachelor's/Master's students in projects and theses.

Requirements

• Essential Ph. D. in Electrical Engineering, Electronics, Robotics, Machine Learning, Computer Science, or other quantitative fields.
• Strong knowledge of the NanoRobotics research domain, including sub-domains such as TinyML and ULP hardware digital design.
• Field-proven capability of conducting excellent scientific work (coding, writing, presentation making, teaching, etc.) even without genAI support, e.g., ChatGPT, Gemini, Claude, etc., and critical thinking skills (strictly required).
• Strong scientific methodology and capability in project definition, scientific discussion, and vision of future evolution of the NanoRobotics research area.
• Strong publication record in AI, embedded systems, machine learning, and robotics in relevant (Q1, A+) scientific venues, e.g., IEEE, ACM, Elsevier, and Springer. A significant (>50%) subset of the candidate publications must be as the first author (strictly required).
• Previous practical/technical experience (track record) in design, training, implementation, optimization, and efficient deployment of deep learning models on ULP embedded platforms.
• Solid expertise in TinyML and miniaturized (nano-sized) robotics platforms is also required.
• Experience with AI compiler frameworks (e.g., Deepploy, NNTool, TfliteMicro).
• Strong experience (track record) in the programming and optimization of ULP multicore MCUs/SoCs, such as Cortex-M4 and ESP32.
• Experience with RISC-V-based processors, in particular GWT’s GAP8/9 (strictly required).
• Proven experience in the programming and field testing of nano-robotic platforms, including the deployment and real-world validation of onboard deep learning algorithms under stringent power and memory constraints.
• Experience with the Bitcraze Crazyflie 2.0/2.1 platform will be considered an asset.
• Proficiency in Python programming for deep learning and C/C++ programming for MCUs. Assembly programming is a plus.
• Experience (track record) in supervising Bachelor's/Master's students in class projects and thesis.
• High motivation, commitment, and strong desire for research and publishing at top conferences and journals.
• Strong motivation and commitment to fulfill research activities, milestones, and deadlines.
• Good organizational skills, ability to work independently, and plan and direct others' work, including Ph.D. students (not limited to).
• Ability to efficiently lead teams and teamwork, including the capability to engage in scientific dialogue with colleagues from other research domains and other members of the Institute.
• Interest in teaching and tutoring students (Bachelor, Master, Ph. D.).
• Proficiency in written and spoken English.

Preferred

• Documented relevant research experiences outside the Ph. D. group.
• Experience with vision-based algorithms (object detection, tracking, or visual navigation) deployed on nano-robotic platforms under tight latency and power budgets.
• Experience in on-device training, continual learning, or adaptive inference for ULP MCUs/SoCs.
• Familiarity with parallel programming (e.g., CUDA) of many-core, multi-core, and heterogeneous systems (e.g., NVIDIA ORIN board, ARM big.LITTLE).
• Familiarity with sensors' drivers (e.g., IMU, TOF) and communication protocols (e.g., I2C, SPI).

What SUPSI Offers

• Fixed-term 1-year contract, with possibility of renewal.
• Attractive salary in line with Swiss standards and candidate experience.
• Workplace at IDSIA USI-SUPSI in the new campus in Lugano-Viganello (Switzerland).
• International and interactive working environment.
• Travel support for participating in top-quality international conferences and workshops.
• Opportunity to develop professional and scientific skills.

Application Process

Applications will only be considered if submitted electronically by 17.05.2026, through the dedicated application form. The following documents, written in English, must be included:

• Motivation letter (1-2 pages), including a brief description of your doctoral research and an outlook on future research directions.
• Two recommendation letters (with e-mail addresses).
• Curriculum vitae with a link to the Ph. D. thesis.
• A complete list of publications, awards, and major open-sourcing releases.
• Transcript of records (Bachelor's and Master's).