The Physical Computation Laboratory is seeking a PhD student to work on a project developing new **Computer Architectures for Probabilistic Robotics/Autonomous Systems Applications**.
The Physical Computation Laboratory (http://physcomp.eng.cam.ac.uk/
) is a new and growing research group that investigates how to use an understanding of the physical world and the flexibility of sensing systems to improve the efficiency of computing systems that interact with nature. We conduct fundamental research augmented with hardware and software prototypes to get the results of our research out into the world. We have created several open source hardware and software platforms that are being used by several research groups and have active research collaborations with several departments across the University of Cambridge (from the Department of Applied Mathematics and Theoretical Physics, to the Department of Zoology) and across the world (from Max Planck to MIT).
The research of the successful applicant will investigate microarchitectures for probabilistic machine learning in Robotics/Autonomous Systems applications. The research will investigate new hardware designs and evaluate the efficacy of the proposed designs and implementations using measurements on state-of-the-art prototype hardware platforms. This will be augmented with analysis of the effect of sensor characteristics (cameras, LIDAR, sonar) on computation-/ data-flow graphs of important computer vision algorithm kernels.
The candidate will join a dynamic and multidisciplinary team of four PhD students, three postdocs, four masters students, and several industrial collaborators.
A successful candidate should have very strong background and excellent grades in their completed undergraduate/masters courses on:
- Digital logic
- Computer architecture
- Digital signal processing
- Probability and statistics
Candidates should also have a strong working knowledge of:
- C/C++ and Python
- Assembly language programming for one or more RISC architectures (e.g., RISC-V, PowerPC, or ARM)
- Git (and a demonstrable record of working with repositories hosted on GitHub)
- Mathematica or Matlab
Familiarity with popular machine learning tools (Caffe2, TensorFlow, TensorFlow Lite, MXNet) is a plus.
Applications should be submitted via the University of Cambridge Graduate Admissions web pages at https://www.graduate.study.cam.ac.uk/courses/directory/egegpdpeg/apply
, with Dr. Phillip Stanley-Marbell specified as the potential supervisor.
To find out more about funding options (the next deadline is 7th January 2020), please see https://www.graduate.study.cam.ac.uk/finance/funding/graduate-funding-competition