LINKÖPING UNIVERSITY advertises a position as

Professor in Computing Architectures for Machine Learning
formally based at the division of Computer Engineering at the Department of Electrical Engineering

Description of the subject area
Linköping University is looking for a full professor in Computing Architectures for Machine Learning. The position is an initiative within the national Wallenberg AI, Autonomous Systems and Software Program. The Wallenberg AI, Autonomous Systems and Software Program (WASP) is Sweden’s largest individual research program ever; a major national initiative for strategically motivated basic research, education and faculty recruitment. WASP comprises five Swedish full partner universities and a substantial part of Swedish industry and commerce. The WASP initiative includes among many instruments an international recruitment program and the present announcement aims to strengthen Linköping University specifically in the field of machine learning within the AI focus-area of WASP.

The primary focus of the present position is design and analysis of digital hardware systems for Machine Learning (ML). This includes hardware/software co-design approaches with a strong connection between the algorithms and the associated programmable hardware, as well as research on new system architectures that significantly improve learning, inference, and decision making. The current ML revolution requires two kinds of scale: in the datasets that are available and the computing resources used to analyze them. Big datasets contain the raw material to understand the world around us, and thus far, large-scale ML computing has mostly been done in large datacenters containing hordes of graphical processing units (GPUs). We are transitioning to an era where datacenters will be filled with computers designed solely for ML computations. Current systems are expensive, energy consuming, and basically limited to one single supplier. In this context, our existing ML experts in the department will work together with the division of Computer Engineering to design new generations of computing systems with the goal of sustainable and energy efficient ML platforms.

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