Are you interested in working on cutting-edge research projects that combine machine learning and neuromorphic computing? Do you have a strong interest in Bayesian neural networks (BNNs) and their applications? If yes, then you might be the perfect candidate for a student research assistant position
at our lab!

Neuromorphic computing is a non-von Neumann computational paradigm that mimics the structure and function of biological brains using neurons and synapses. In-memory computing is a technique that performs certain computational tasks in place in memory, thereby reducing the energy cost of data movement. By combining these two approaches, we aim to achieve high performance and efficiency for neural networks (NNs) inference. However, as with any new technology, there are still many challenges that need to be addressed, such as ensuring the test and reliability of neuromorphic in-memory computation with emerging non-volatile memories. Investigating the test and reliability aspects of neuromorphic in-memory computation is therefore essential to ensure that this technology can beused effectively in a wide range of applications.

equirements:
   •  A bachelor’s or master’s degree in computer science, electrical engineering, or related fields
   •  A good understanding of NNs concepts or willingness to learn
   •  Experience with PyTorch or willingness to learn
   •  Motivated to get familiarized with neuromorphic computing concepts and architectures.
   •  Good programming skills in Python

Contact:
   •  Prof. Dr. Mehdi Tahoori, Chair of Dependable Nano Computing
   •  Soyed Tuhin Ahmed, Chair of Dependable Nano Computing

References:
   1.  F. Staudigl, F. Merchant and R. Leupers, "A Survey of Neuromorphic Computing-in-Memory: Architectures, Simulators, and Security," in IEEE Design & Test, vol. 39, no. 2, pp. 90-99, April 2022, doi: 10.1109/MDAT.2021.3102013.
   2.  Soyed Tuhin Ahmed, Michael Hefenbrock, Christopher Münch, Mehdi B. Tahoori: NeuroScrub+: Mitigating Retention Faults Using Flexible Approximate Scrubbing in Neuromorphic Fabric Based on Resistive Memories in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2022.
   3.  Soyed Tuhin Ahmed, Mehdi B. Tahoori: Compact Functional Test Generation for Memristive Deep Learning Implementations using Approximate Gradient Ranking, IEEE International Test Conference (ITC), 2022.