Location : Aarhus N, Denmark
Yearly income : https://phd.arts.au.dk/financing/salary-and-employment
Research area and project description (For application please check the following link: https://phd.tech.au.dk/for-applicants/apply-here/ Artificial neural networks are already having a very tangible effect in society. Despite the impressive progress in software, hardware is lagging behind by a decade or even more as it is not customized for implementation of such neural networks. Implementation of advanced neural network algorithms on general-purpose CPUs and GPUs is not energy-efficient. This can be overcome with the development of fully custom-designed hardware for implementation of neural networks with a vision of significantly improved energy-efficiency and performance in such a way that it resembles biological brains for specific applications such as complex signal processing and pattern recognition. Towards this goal, custom-designed CMOS implementation of artificial neurons and synapses have been researched and some progresses have been achieved. However, the CMOS implementation of neural networks is still not energy- and area-efficient , which will limit the scalability of such networks. These issues have driven a significant effort on investigation of non-CMOS implementations of neuromorphic computing systems, which include synapses implemented using memristors [2,3], Magnetic Tunnelling Junctions (MTJs) etc., and artificial neurons using MTJs, Spin-Torque Nano-Oscillators (STNOs) , mutually synchronized Spin-Hall Nano-Oscillators (SHNOs) , etc.
Among different technologies, Spintronics is a strong contender as it is compatible with CMOS, multifunctional, extremely versatile with features like non-volatility, plasticity and oscillatory behaviour, which can be exploited to implement artificial neural components to develop energy-efficient neural networks. Despite some progress in spintronic NCS [4, 6], challenges such as lack of proper artificial neuron and synapse implementation resembling biological neural components, low speed, lack of any forward-looking architecture implementation, lack of proper algorithms for spintronic networks, lack of any simulation platforms that combine libraries from several emerging technologies together for a better assessment of larger networks, have hindered scaling of complex architectures .
Within an interdisciplinary project, SpinAge, funded by H2020 , we aim at development of a brain-inspired (neuromorphic computing) networks using synchronized spintronic oscillators  together with other European research teams  leading to orders of magnitudes improvement in performance. For this position, we aim at development of analytical and numerical models for spintronic devices (oscillators) [10-12] and bringing them into a simulation platform enabling simulation of larger neural networks, which requires a good understanding of the physics behind the spintronic oscillators, and skills in development of analytical models for such devices.
 J. Kim et al., Proc. of the IEEE, 103 (1), 106-130, (2015).
 S. Ambrogio et al., Nanotechnology 24, 384012, (2013).
 S. Saïghi et al., Front. Neurosci. 9:51, (2015).
 J. Torrejon, , et al., Nature 547.7664, 428-431, (2017)
 A. Awad et al., Nature Phys. 13, 292, (2017)
 H. Farkhani, et al. Frontiers in Neuroscience 13 (2019).
 Grollier, J., et al. Nature Electronics (2020): 1-11.
 M. Zahedinejad, et al. Nature Nanotechnology 15.1, 47-52, (2020).
 S. G. Ramasubramanian, et al. ISLPED. IEEE, (2014).
 D. I. Albertsson, et al. IEEE Transactions on Magnetics 55.10, 1-8, (2019).
 J. Pelloux-Prayer, and Farshad Moradi. IEEE Transactions on Electron Devices (2020).