PhD's on neuromorphic computing & engineering: algorithms&hardware architectures

The Neuromorphic Edge Computing Systems (NECS) Lab at the Eindhoven University of Technology invites applications for multiple Ph.D. positions from passionate and innovative young researchers. Our mission is to overstep the boundaries of artificial intelligence (AI) and neural networks, challenging the current computing paradigms by drawing inspiration from the most sophisticated system known—the human brain.

Despite significant advances in AI, current systems must catch up to the animal kingdom's efficiency in processing complex, real-time tasks with minimal energy in uncertain conditions. This limitation stems from a fundamental difference in design philosophy; while nature leverages distributed processing and inherent noise tolerance, modern computing relies on deterministic, bit-perfect operations with a clear separation between memory and computation. To bridge this gap, the NECS Lab is pioneering research into brain-inspired computing models that mimic the natural neural system's computational physics. Our research entails the development of novel brain-inspired computing theories, learning systems, and the design of ultra-low-power circuits, systems, and computing architectures.

We are seeking candidates for several PhD positions, each focusing on one or more of the following cutting-edge research topics:

1. Ultra-low-power Mixed-signal Circuits and Architecture: Design of novel CMOS technologies for neural networks embodying the brain's adaptive capabilities. Projects aim to replicate the self-healing abilities of neural systems, which are distinctive traits of biological neural systems.
2. Event-based Sensing & Computing: Development of event-based sensory perception systems, with a focus on low-power radar sensing for applications in autonomous vehicles and robotics. This research will pioneer a brain-inspired framework for active perception.
3. Spike-based Neuromorphic Hardware for Machine Intelligence: Embedding complex AI functionalities into neuromorphic hardware augmented by emerging technologies (e.g., ReRAM, Phase-Change Memories) for increasing the energy efficiency of current deep learning models. This involves integrating cutting-edge nanomaterials into architectures optimized for AI.
4. Bio-inspired Online Learning Algorithms: Creation of spike-based online learning algorithms for real-time adaptation to hardware degradation and input changes. This includes developing unsupervised methods for drift compensation in physical systems.

With these projects, the NECS lab aims to fundamentally understand neural and cognitive processes in the AI domain. We strive to have a tremendous impact in the computer sciences, electronics, and robotics sectors, paving the way for a new generation of neuro-inspired processors for efficient autonomous agents.

Job Description

We offer an exciting Ph.D. opportunity for a pioneering approach to neuromorphic engineering and computing, drawing inspiration from neural mechanisms observed in the animal kingdom. These cross-disciplinary projects seek to revolutionize sensing, computing, and learning by adopting the computational primitives found in natural neural systems: the massively parallel networks of spiking neurons and adaptive synapses.

Key responsibilities:

• Engage in groundbreaking research to develop advanced neuromorphic sensing, and computing systems by taking inspiration from the sensing and computing capabilities of natural neural systems.
• Innovate in the areas of signal encoding, processing pipelines, and embedded computer architectures to emulate bio-inspired computational models effectively.
• Explore and implement beyond traditional deep learning techniques, moving towards the next generation of neuromorphic technologies. This includes leveraging spiking neural networks' efficiency, parallelism, and adaptability for enhanced sensing and computing.
• Delve into new bio-inspired theories and connect them to hardware architectures.

Requirements:

• A master's degree (or an equivalent university degree) in Electrical Engineering, Computer Science, Physics, or related fields, with an interest in neuromorphic computing and engineering.
• Has a strong background in one or more of the following:
• Analog mixed-signal designs, devices, and general knowledge of semiconductor physics, modeling, and electronic system design.
• Knows circuit simulation and VLSI design (spice, cadence, Synopsys tools).
• Experience with hardware architecture, FPGA, and system-level simulation is desirable.
• A good theoretical understanding of statistics and machine learning theory.

• Strong analytical skills and proficiency in programming and simulation tools.
• Ability to work independently and collaboratively in a multidisciplinary team.
• Motivated to develop your teaching skills and coach students.
• Fluent in spoken and written English (C1 level).      

Salary Benefits:

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%.
• 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.

38 hours per week

De Rondom 70