PhD Position: Bridging Analog and Digital Design in Neuromorphic Systems

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As part of a new MSCA Doctoral Network “ELEVATE” (101227453), we are offering a:


PhD Position: Bridging Analog and Digital Design in Neuromorphic Systems


Location: Institute of Neuroinformatics, University of Zurich and ETH Zurich, Switzerland.


Earliest starting date: January 1st 2026.


The EIS lab (https://www.ini.uzh.ch/en/research/groups/EIS.html) at the Institute of Neuroinformatics (https://www.ini.uzh.ch/en) invites applications for PhD student position to work at the intersection of Electrical Engineering, Computational Neuroscience, and Machine Learning to help building efficient sequence processing systems at multiple time scales. 

 

Research Background

At the EIS lab, we are neuromorphic engineers who are curious to explore how motifs from the brain’s morphology and architecture can serve as useful inductive biases to (i) improve energy and area efficiency of the neural network hardware substrates, and (ii) improve generalization, task performance and learning of AI systems. Our interests are therefore at the intersection of NeuroAI and hardware design.

 

Project description

There has been a long-standing debate between analog and digital design methodologies in neuromorphic engineering.

  • - Analog systems can directly implement ordinary differential equations that describe continuous physical dynamics, resembling the brain’s computational primitives (synapses, neurons, populations). However, they are often sensitive to noise and device variability.
  • - Digital systems simulate these dynamics numerically. While this may require more computation, digital systems are more robust, easier to design, and less prone to noise.

This project aims to bridge the gap between analog and digital approaches, combining the strengths of both.

  • - At the physical level, sensory inputs will be processed using analog circuits with memristive devices, implementing sequence models that extract both short-term and long-term dynamics.
  • - At the functional level, higher-order cognitive processes such as decision making, perception, and motor control will be carried out using digital circuits, leveraging the features extracted by the analog frontend.

The successful candidate will explore this hybrid paradigm, working across device physics, circuit design, and computational models, with the goal of delivering architectures that combine the efficiency of analog computing with the flexibility and robustness of digital systems.

Eligibility

The project is part of the ELEVATE grant under Marie Skłodowska-Curie Actions (MSCA) training network, where PhD students get to work in collaboration between 10 academic and 10 industrial partners:

https://www.elevate-dn.eu/index.php/about/

Applicants must comply with the MSCA mobility rules: they must not have resided or carried out their main activity (work, studies, etc.) in Switzerland for more than 12 months in the last 36 months.

 

Required Profile

  • - MSc in electrical engineering, physics or related fields.
  • - Strong analog/digital circuit design background (expertise with Cadence Virtuoso and Innovus is a must). 
  • - Previous experience with tape-outs is a big plus.
  • - Some understanding of current AI models is a big plus. 
  • - Curiosity and critical thinking across disciplines.
  • - Passion for understanding biological intelligence and building artificial ones.
  • - The desire and collaborative spirit required to work closely in an interdisciplinary environment (on a daily basis you will work closely with neuroscientists, computer scientists, physicists and electrical engineers).
  • - Excellent written and oral communication skills in English.

 

Your job

  • - Conceive, design, simulate, layout and tape-out low-power analog and digital circuits for time-series processing.
  • - Test and Interface ICs with the real-world signals.
  • - Quantitative study of where and when to use analog and digital circuit design in an end-to-end setting, going from perceiving sensory signals to functions such as motor control or classification.
  • - Publish research articles, regular participation in top international conferences to present your work.
  • - Complete two internships:
    • - - TU Delft for investigation of digital implementation of higher order cognitive functions (e.g., motor control) (Prof. Charlotte Frenkel, co-supervisor)
    • - - ZHAW (Prof. Yulia Sandamirskaya) for demonstration of the digital/analog system in sensory-motor applications.
  • - Participate in yearly retreats organized by the doctoral network participants.
  • - Supervise student projects and BSc/MSc theses.
  • - Serve as teaching assistant for two semesters.



Salary and conditions

The salary and benefits will follow the standards set by the University of Zurich for PhD students. PhD students will receive their degree from both the University of Zurich and the ETH Zurich.

 

Application process

Interested candidates should submit a detailed CV, a cover letter explaining their interest in the position (1 or 2 pages long), a transcript for your BSc and MSc degrees, and the contact information of 2 references to melika@ini.uzh.ch with subject line: “ELEVATE R6 PhD”.

We strive to build a diverse work environment and encourage applications from all qualified individuals irrespective of their gender, age, cultural background, and disability status. Only candidates shortlisted for this position will be contacted for an interview.

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