Postdoctoral Fellow in Computer Science (Harvard University)

Position Description Project Deep learning plays an essential role in the operation of an autonomous vehicle (AV), enabling automated detection, prediction, mapping, and planning. During operation, data is collected through a variety of sensors in an AV—including RADAR, LIDAR, cameras, and other advanced driver assistance systems (ADAS) sensors. These sensors generate vast amounts of concurrent data, requiring real-time processing (latency-bound throughput) to ensure vehicle safety. A crucial challenge is achieving this while maintaining low power consumption. The main objective of the project is to develop a complete end-to-end high-performance DNN system for on-premise computing applications—mainly for a SWaP-constrained AV—using hybrid electro-photonic accelerators. The project aims to design and prototype a complete electro-photonic computing (EPiC) system (CPUs + accelerators), integrate it with AV sensors, and demonstrate its ability to perform perception, mapping, and planning while overcoming the power and performance limitations of CMOS-only computers. The end goal is to demonstrate a fully autonomous buggy powered by the EPiC system. This project is a collaboration among Lightmatter, Boston University, and Harvard University. The Lightmatter team is led by Dr. Darius Bunandar, the Boston University team by Prof. Ajay Joshi, and the Harvard University team by Prof. Vijay Janapa Reddi. Role Two postdoctoral researcher positions are available. The researchers will be jointly supervised by Prof. Joshi (Boston University) and Prof. Reddi (Harvard University). Appointment: One year, with the possibility of a second year (subject to performance and funding). Expected Start Date: January 1, 2022. Responsibilities Collaborate with graduate students at Boston University and Harvard University, and engineers at Lightmatter to achieve project deliverables. Publish research in top-tier conferences; present at conferences, universities, and companies. Contribute to the development of new research projects and grant proposals. Basic Qualifications Applicants must have a Ph.D. in Electrical Engineering, Computer Engineering, or Computer Science (by the start date) with expertise in at least one of the following areas: Computer Architecture/Systems: Design, modeling, simulation, and/or physical design of heterogeneous system/processor architectures; silicon-photonic computing architectures; silicon-photonic network architectures. Machine Learning Algorithms/Systems: Design and use of ML algorithms; applying ML to computing system design. Autonomous Vehicles/Systems: Design, integration, and evaluation of AI models for autonomous driving systems; experience with AV simulators and the AV stack. Additional Qualifications Expertise in two or more of the above areas is a plus. Hands-on experience with design and prototyping of complete computing systems will be considered a plus. Special Instructions Applicants should submit the following documents: Curriculum Vitae (CV) with a list of publications. Cover Letter summarizing how your background/experiences make you a good fit for the project, and outlining your career goals. Undergraduate and graduate transcripts. Contact Information Contact Person: Sarah Gayer Email: sgayer@g.harvard.edu Equal Opportunity Employer Harvard University is committed to equal opportunity and non-discrimination. We seek talent from all parts of society and the world, and we strive to ensure everyone at Harvard thrives. Our differences help our community advance Harvard’s academic purposes. Harvard’s equal employment opportunity and non-discrimination policies prohibit discrimination on the basis of race, sex, ethnicity, color, national origin, religion, disability, or any other characteristic protected by law or identified in the university’s policy. These policies ensure all community members can participate fully in work and campus life free from harassment and discrimination. References Minimum Number Required: 2 Maximum Number Allowed: 5 Keywords On-Premise Computing \| Autonomous Vehicles \| Computer Architecture \| Machine Learning \| Runtime Systems Applicant Documents Required Documents Curriculum Vitae Cover Letter Transcript Optional Documents None If you apply for this position please say you saw it on Academicoxy

Position Description

Project
Deep learning plays an essential role in the operation of an autonomous vehicle (AV), enabling automated detection, prediction, mapping, and planning. During operation, data is collected through a variety of sensors in an AV—including RADAR, LIDAR, cameras, and other advanced driver assistance systems (ADAS) sensors. These sensors generate vast amounts of concurrent data, requiring real-time processing (latency-bound throughput) to ensure vehicle safety. A crucial challenge is achieving this while maintaining low power consumption.

The main objective of the project is to develop a complete end-to-end high-performance DNN system for on-premise computing applications—mainly for a SWaP-constrained AV—using hybrid electro-photonic accelerators. The project aims to design and prototype a complete electro-photonic computing (EPiC) system (CPUs + accelerators), integrate it with AV sensors, and demonstrate its ability to perform perception, mapping, and planning while overcoming the power and performance limitations of CMOS-only computers. The end goal is to demonstrate a fully autonomous buggy powered by the EPiC system.

This project is a collaboration among Lightmatter, Boston University, and Harvard University. The Lightmatter team is led by Dr. Darius Bunandar, the Boston University team by Prof. Ajay Joshi, and the Harvard University team by Prof. Vijay Janapa Reddi.

Role
Two postdoctoral researcher positions are available. The researchers will be jointly supervised by Prof. Joshi (Boston University) and Prof. Reddi (Harvard University).

Appointment: One year, with the possibility of a second year (subject to performance and funding).
Expected Start Date: January 1, 2022.

Responsibilities

Collaborate with graduate students at Boston University and Harvard University, and engineers at Lightmatter to achieve project deliverables.
Publish research in top-tier conferences; present at conferences, universities, and companies.
Contribute to the development of new research projects and grant proposals.

Basic Qualifications

Applicants must have a Ph.D. in Electrical Engineering, Computer Engineering, or Computer Science (by the start date) with expertise in at least one of the following areas:

Computer Architecture/Systems: Design, modeling, simulation, and/or physical design of heterogeneous system/processor architectures; silicon-photonic computing architectures; silicon-photonic network architectures.
Machine Learning Algorithms/Systems: Design and use of ML algorithms; applying ML to computing system design.
Autonomous Vehicles/Systems: Design, integration, and evaluation of AI models for autonomous driving systems; experience with AV simulators and the AV stack.

Additional Qualifications

Expertise in two or more of the above areas is a plus.
Hands-on experience with design and prototyping of complete computing systems will be considered a plus.

Special Instructions

Applicants should submit the following documents:

Curriculum Vitae (CV) with a list of publications.
Cover Letter summarizing how your background/experiences make you a good fit for the project, and outlining your career goals.
Undergraduate and graduate transcripts.

Contact Information

Contact Person: Sarah Gayer
Email: sgayer@g.harvard.edu

Equal Opportunity Employer

Harvard University is committed to equal opportunity and non-discrimination. We seek talent from all parts of society and the world, and we strive to ensure everyone at Harvard thrives. Our differences help our community advance Harvard’s academic purposes.

Harvard’s equal employment opportunity and non-discrimination policies prohibit discrimination on the basis of race, sex, ethnicity, color, national origin, religion, disability, or any other characteristic protected by law or identified in the university’s policy. These policies ensure all community members can participate fully in work and campus life free from harassment and discrimination.