Interpreting complex sensory data patterns in real-time with minimum power consumption is important for the survival of any living creature. The brain is responsible for performing this computation and has evolved over millions of years to be efficient in power consumption and processing speed. A honey bee's brain uses a few milliwatts of power, yet it can perform a wide range of complex tasks such as navigation, communication, learning, and memory in real-time. Today, the most advanced commercial processor technology for this task consumes several orders of magnitude higher energy than the honey bee's brain. Neuromorphic devices are seen as the way forward towards more effective and more efficient machine learning. However, current on-device learning in embedded AI processors (including bio-inspired neuromorphic systems1-2-3) are a luxury feature that consumes a significant amount of power without considering power reduction. However, as humans, we know that, with practice, we can perform tasks better and faster with less effort. Our goal is to design methods and tools that leverage continuous learning to reduce power consumption and latency by algorithm-hardware co-optimization. As a candidate for this interdisciplinary Ph.D. position, you will be at the forefront of a transformative exploration into embedded AI. Your

work will focus on developing open-source algorithms and hardware designs that embody the principles of neuromorphic engineering, pushing the boundaries of what is technically possible. We invite innovative thinkers who are passionate about combining the efficiency of biological systems with cutting-edge technology to apply. 1- Tang, Guangzhi, et al. "SENECA: Building a fully digital neuromorphic processor, design trade-offs and challenges." Frontiers in Neuroscience 17: 1187252. 2- Davies, Mike, et al. "Loihi: A neuromorphic manycore processor with on-chip learning." Ieee Micro 38.1 (2018): 82-99. 3- Rostami, Amirhossein, et al. "E-prop on SpiNNaker 2: Exploring online learning in spiking RNNs on neuromorphic hardware." Frontiers in Neuroscience 16 (2022): 1018006.

• MSc degree in electrical/computer engineering or associated field;
• Knowledge of computer architecture;
• Basic knowledge of digital hardware design; practical experience is a plus;
• Basic knowledge of standard deep learning algorithms is a plus;
• English proficiency in speaking and writing;

• As a PhD candidate at UT, you will be appointed to a full-time position for four years, with a qualifier in the first year, within a very stimulating and exciting scientific environment; ·
• The University offers a dynamic ecosystem with enthusiastic colleagues;
• Your salary and associated conditions are in accordance with the collective labour agreement for Dutch universities (CAO-NU); ·
• You will receive a gross monthly salary ranging from € 2.770,- (first year) to € 3.539,- (fourth year); ·
• There are excellent benefits including a holiday allowance of 8% of the gross annual salary, an end-of-year bonus of 8.3%, and a solid pension scheme; ·
• The flexibility to work (partially) from home;
• A minimum of 232 leave hours in case of full-time employment based on a formal workweek of 38 hours;
• A full-time employment in practice means 40 hours aweek, therefore resulting in 96 extra leave hours on an annual basis;
• Free access to sports facilities on campus;
• A family-friendly institution that offers parental leave (both paid and unpaid); ·
• You will have a training programme as part of the Twente Graduate School where you and your supervisors will determine a plan for a suitable education and supervision; ·
• We encourage a high degree of responsibility and independence, while collaborating with close colleagues, researchers and other staff.

The PhD student will join CAES, a group working on the most efficient and effective computer architectures of the future, from large-scale data-centre servers to low-power/small-scale embedded systems. The group's research sits at the border between EE and CS, aiming to bridge any gaps between these two sides of computing systems.