PhD Analog/mixed-signal design for oscillatory neural networks

PhD Analog/mixed-signal design for oscillatory neural networks

Published Deadline Location
13 Jul 16 Oct Eindhoven

You cannot apply for this job anymore (deadline was 16 Oct 2023).

Browse the current job offers or choose an item in the top navigation above.

Job description

The NanoComputing Research Lab in Integrated Circuits (IC) group within the Department of Electrical Engineering of the Eindhoven University of Technology (TU/e) is seeking to hire an outstanding PhD candidate within the Horizon Europe project PHASTRAC.


In recent years, we have witnessed an explosion of artificial intelligence (AI) applications which will continue to grow over the next decade. An intelligent and digitized society will be ubiquitous, enabled by increased advances in nanoelectronics. Key drivers will be sensors interfacing with the physical world and taking appropriate action in a timely manner while operating with energy efficiency and flexibility to adapt. The vast majority of sensors receive analog inputs from the real world and generate analog signals to be processed.

However, digitizing these signals not only creates enormous amount of raw data but also require a lot of memory and high-power consumption. As the number of sensor-based IoTs grows, bandwidth limitations make it difficult to send everything back to a cloud rapidly enough for real-time processing and decision-making, especially for delay-sensitive applications such as driverless vehicles, robotics, or industrial manufacturing.

In this context, PHASTRAC proposes to develop a novel analog-to-information neuromorphic computing paradigm based on oscillatory neural networks (ONNs). We propose a first-of-its-kind and novel analog ONN computing architecture to seamlessly interface with sensors and process their analog data without any analog-to-digital conversion. ONNs are biologically inspired neuromorphic computing architecture, where neuron oscillatory behavior will be developed by innovative phase change VO2 material coupled with synapses to be developed by bilayer Mo/HfO2 RRAM devices. PHASTRAC will address key issues:
  1. Novel devices for implementing ONN architecture,
  2. Novel ONN architecture to allow analog sensor data processing, and
  3. Processing the data efficiently to take appropriate action.
The PHASTRAC consortium includes some of Europe's strongest research groups and industries, covering from device fabrication, circuit and architecture design to end-use applications. We will demonstrate a first-of-its-kind analog-to-information computing paradigm with industrial applications such as intelligent vehicle interior design and human-robotics interactions that opens the road for EU leadership in energy efficient edge computing.


Eindhoven University of Technology (TU/e)


We are seeking highly motivated and talented PhD candidates to join our research team in the exciting field of analog/mixed-signal circuit design for neuromorphic computing based on oscillatory neural networks. This position offers an exceptional opportunity to contribute to cutting-edge research in the development of novel brain-inspired computing architectures for advanced machine learning and artificial intelligence applications.

  • Conduct research on analog/mixed-signal circuit design methodologies for neuromorphic computing based on oscillatory neural networks.
  • Design in CMOS technology nodes (e.g., 65nm, 16nm or 7nm) and optimize analog circuits for implementing oscillatory neural network models, including oscillators and synaptic circuits. Tapeout via Europractice.
  • Develop innovative techniques to achieve low-power and high-speed operation in neuromorphic circuits.
  • Collaborate with multidisciplinary teams to integrate the designed circuits into larger neuromorphic systems.
  • Perform extensive simulations and evaluations to assess the performance and efficiency of the designed circuits.
  • Analyze and interpret simulation results, and contribute to scientific publications and conferences and project technical reports.
  • Stay abreast of the latest advancements in analog/mixed-signal circuit design for neuromorphic computing and contribute to the knowledge base of the research field.

  • Master's degree in Electrical Engineering, Computer Engineering, or a related field.
  • Strong background in analog/mixed-signal circuit design, including experience with CMOS technology and VLSI design.
  • Knowledge of neuromorphic computing principles and familiarity with oscillatory neural network models is highly desirable.
  • Proficiency in analog design tools, such as Cadence or Synopsys, for schematic entry, simulation, and layout.
  • Experience with circuit-level simulations, noise analysis, and characterization of analog circuits e.g., monte-carlo analysis.
  • Solid understanding of signal processing, neural networks, and machine learning concepts.
  • Programming skills in languages such as MATLAB, Python, or C/C++.
  • Excellent analytical and problem-solving skills
  • Good communication and teamwork abilities, with a passion for research and an eagerness to learn and contribute to the field.

Conditions of employment

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:
  • Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
  • Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. €2,770 max. €3,539).
  • A year-end bonus of 8.3% and annual vacation pay of 8%.
  • Should you come from abroad and comply with certain conditions, you can make use of the so-called '30% facility', which permits you not to pay tax on 30% of your salary.
  • A broad package of fringe benefits, including an excellent technical infrastructure, moving expenses, and savings schemes.
  • High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
  • An excellent technical infrastructure, on-campus children's day care and sports facilities.
  • An allowance for commuting, working from home and internet costs.
  • A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.


  • PhD
  • Engineering
  • max. 38 hours per week
  • University graduate
  • V36.6769


Eindhoven University of Technology (TU/e)

Learn more about this employer


De Rondom 70, 5612 AP, Eindhoven

View on Google Maps

Interesting for you