We are seeking a highly skilled researcher with a PhD in electrical or electronics engineer/computer science engineer/physicist with experience in areas such as analog or digital circuit design, embedded systems, systems on chip, FPGA programming or artificial intelligence. The candidate will join the staff of the Institute of Microelectronics of Seville (IMSE), an academic research center belonging to the University of Seville and the Spanish Research Council (CSIC). IMSE is equipped with state-of-the-art infrastructures hosting 1.000m^2 of laboratories for the design and test of electronic circuits and opto-electronic sensors. A new clean room facility for advanced integrated circuits packaging and additive manufacturing is currently being set up. It is located in a technological park at 15 minutes walking from Sevilla city center. The candidate is sought to join the Neuromorphic Systems Group, which has over 30 years of experience in the field of neuromorphic hardware systems and applications, including the development of spatial contrast retinas, dynamic vision sensors, convolutional neural processors, spiking convolutional neural networks, spiking learning circuits and algorithms, and spiking neural processors combining conventional CMOS circuits with nanodevices.

In this talk, I aim to show that the dynamical properties of emerging nanodevices can accelerate the development of smart, and environmentally friendly chips that inherently learn through their physics. The goal of neuromorphic computing is to draw inspiration from the architecture of the brain to build low-power circuits for artificial intelligence. I will first give a brief overview of the state of the art of neuromorphic computing, highlighting the opportunities offered by emerging nanodevices in this field, and the associated challenges. I will then show that the intrinsic dynamical properties of these nanodevices can be exploited at the device and algorithmic level to assemble systems that infer and learn though their physics. I will illustrate these possibilities with examples from our work on spintronic neural networks that communicate and compute through their microwave oscillations, and on an algorithm called Equilibrium Propagation that minimizes both the error and energy of a dynamical system.