PhD on Energy-Efficient Online Learning Using Compute-in-Memory Architectures

Introduction

This PhD project, part of the REACT MSCA Doctoral Network, aims to develop an energy-efficient compute-in-memory (CIM) architecture using gain-cell memory for real-time edge learning, addressing power, latency, and memory bandwidth issues with reliable fault detection.

Job Description

Self-awareness in humans is an innate capability, arising from the brain’s ability to process a multitude of sensory inputs. Emulating this functionality in electronic systems, commonly referred to as neuromorphic computing holds the potential to create highly intelligent machines capable of supporting a wide range of everyday applications, from autonomous vehicles to smart navigation systems. However, realizing neuromorphic computing in practice presents significant challenges, particularly in the areas of energy efficiency, reliability, and security.

The REACT Marie Skłodowska-Curie Actions (MSCA) Doctoral Network addresses the above mentioned challenges by developing a neuromorphic platform that is inherently self-aware in terms of energy consumption, secure operation, and system reliability.

Are you enthusiastic about the opportunity to join the REACT Doctoral Network, be involved in impactful research whilst providing your career an international perspective and boost?

As part of the REACT initiative, 15 early-stage researchers (ESRs) will be trained through a comprehensive, interdisciplinary program spanning material science, device physics, computer architecture, hardware prototyping, compiler design, simulation and emulation tools, as well as cybersecurity, reliability, and system verifiability.

The objective of this PhD project is to develop a gain-cell memory-based compute-in-memory (CIM) architecture to enable energy-efficient online learning at the edge. Conventional edge devices are limited by constraints in power, latency, and memory bandwidth, which pose significant challenges to real-time learning when using traditional von Neumann architectures. By leveraging gain-cell memory, which offers high density and low leakage, and integrating mixed-signal computation directly into memory arrays, the proposed approach significantly reduces data movement and energy consumption. To ensure dependable operation under real-world conditions, efficient fault detection and recovery mechanisms will also be evaluated and integrated, addressing the susceptibility of analog and in-memory computing to noise, process variation, and soft errors. The primary objective is to design a CIM system capable of performing key learning operations, such as vector-matrix multiplication and weight updates, within the memory itself, thereby enhancing energy efficiency and computational throughput. This architecture will be optimized for lightweight, adaptive learning tasks commonly encountered in edge scenarios, such as sensor fusion.

REACT offers a uniquely structured training environment, combining academic excellence with industrial collaboration. ESRs will benefit from close mentorship by leading researchers and industry experts, while also developing essential skills in scientific writing, research ethics, time management, and entrepreneurship. By the conclusion of the REACT project, participants will be well-equipped to pursue impactful careers across academia and industry, with the REACT program serving as a strong foundation for their future success. More information about the project can be found here https://project-react.eu/.

Job Requirements

• Mobility Rule: Candidates must not have resided or carried out their main activity in the Netherlands for more than 12 months in the 3 years immediately before the recruitment date.
• PhD Rule: Applicants must not already possess a doctoral degree at the date of recruitment.​
• Master degree or equivalent in Electrical Engineering with excellent grades.
• Knowledge of Digital/Mixed-Signal Integrated Circuit (IC) design and Computer Architecture.
• Solid skills in HDL (Verilog, VHDL) and scripting languages (Python, TCL).
• Experience with commercial EDA tools (Cadence/Mentor Graphics).
• Knowledge in Neuromorphic architectures and Low power IC design would be a definite plus.
• A research-oriented attitude, is capable of taking initiative, and has a strong problem-solving attitude. Ability to work in an interdisciplinary team.           
• Motivated to develop your teaching skills and coach students.
• Fluent in spoken and written English (C1 level). 

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 assessment after nine months. You will spend a minimum of 10% of your four-year employment on teaching tasks, with a maximum of 15% per year of your employment. 
• 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. € 2,901 max. € 3,707).  
• A year-end bonus of 8.3% and annual vacation pay of 8%. 
• 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. 

Information

Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager Dr. Manil Dev Gomony (m.gomony@tue.nl).

Visit our website for more information about the application process or the conditions of employment. You can also contact Kevin Caris, HR Advisor (k.t.caris@tue.nl or +31 40 2478835).

Are you inspired and would like to know more about working at TU/e? Please visit our career page.

Application

Interested candidates should submit the following documents online through the application link in this website, and concurrently at the application portal in the project website https://project-react.eu/vacancies/.

• A clearly written cover letter (maximum one page) explaining your motivation for applying.
• A detailed curriculum vitae (CV) in English, including a list of publications (if applicable).
• Copies of official BSc/MSc degree certificates and corresponding academic transcripts for each degree.
• Results of your IELTS or TOEFL test (or equivalent).

We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.