Vacancy No. IPE 12-18

IPE 12-18 Bachelor and Master Theses in Physics/Electrical Engineering: Development of FPGA-based machine learning algorithms for High-Energy Physics

 

Job description

Machine learning builds on ideas in computer science, statistics, and optimization. It aims on the development of algorithms to identify patterns and regularities in data, and use these learned patterns to make predictions for future observations. Boosted by its industrial and commercial applications, the field of machine learning is quickly expanding. In this thesis modern machine learning approaches, such as deep learning, should be applied to the analysis of High Energy Physics data. Current trends in FPGA design tools have made them more compatible with the high-level software making FPGAs more accessible to those who build and deploy models. Xilinx’s Zynq SoC/MPSoC appears to be an ideal platform for machine learning. Xilinx’s reVISION Stack removes traditional design barriers by allowing you to quickly take a trained network and deploy it on Zynq SoC and MPSoC for inference.

  • Study the Xlinix Zynq FPGA platform and its development tools
  • Build up a test bench of machine learning algorithms
  • Establish a workflow to implement new machine learning by Xilinx reVISION framework
Personal qualification
  • Knowledge in C and Verilog/VHDL (basic)
  • Embedded and hardware programming (better but not required)
  • Previous experience with developing for a Xilinx Zynq SoC (better but not required)
Organizational unit

Institute for Data Processing and Electronics (IPE)

Starting date

on appointment

Contract duration

according to the study regulations

Contact person

Dr. Ing. Michele Caselle, Institute IPE
Telefon: 0721/608-25903  (michele.caselle@kit.edu)

Application

Please apply online using the button below for this vacancy number IPE 12-18.
Personnel support is provided by 

Ms Schaber
phone: +49 721 608-25184,

Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany

If qualified, handicapped applicants will be preferred.