Research Associate (Limited Term) (2 Year Term, 40% FTE)

Date Posted: 02/14/2025
Req ID: 41662
Faculty/Division: Faculty of Applied Science & Engineering
Department: Department of Electrical & Computer Engineering
Campus: St. George (Downtown Toronto)

Description

The Edward S. Rogers Sr. Department of Electrical & Computer Engineering is Canada’s top-ranked ECE department, and one of the largest. We are home to 100 active and Emeritus professors, more than 1,400 undergraduate and 800 graduate students. Many of our faculty members are world leaders in their field and have been recognized as the brightest and most promising scientists and engineers across the country.

The evolution of cloud-native platforms and software-defined infrastructures has introduced a new paradigm for autonomous service orchestration, resource management, scalable infrastructure, and smart application deployment. This project aims to advance orchestration frameworks, intelligent workload management, and adaptive resource provisioning in large-scale, distributed environments. The selected candidate will be supervised by Prof. Alberto Leon-Garcia.

The research will explore the design and development of autonomous orchestration frameworks for next-generation computing, emphasizing multi-cloud environments, decentralized service orchestration, and autonomous system management across various use cases. By integrating real-time stream processing, analytics, and machine learning approaches, the project seeks to optimize service and infrastructure performance, enhance security, and enable intelligent decision-making throughout the lifecycle of cloud-native applications.

Responsibilities

As part of the Network Architecture Lab (NAL), the Research Associate will:

1. Design and develop orchestration, analytics, and machine learning algorithms for heterogeneous resource management, autonomous infrastructure control, and adaptive workload scaling.
2. Implement intelligent workload scheduling, predictive scaling mechanisms, and event-driven automation for cloud-native platforms.
3. Develop software-defined resource provisioning systems using real-world data from multi-cloud infrastructures and smart environments.
4. Optimize the performance and scalability of orchestration frameworks for large-scale distributed computing.
5. Contribute to research publications, technical reports, and open-source frameworks in software-defined platforms, autonomous computing, and intelligent cloud-native architectures.

Qualifications

A Ph.D. in Computer Science, Computer/Network Engineering, or a related field (or nearing completion) with a robust research foundation in cloud-native computing, distributed systems, machine learning–driven orchestration, and software-defined infrastructure. The ideal candidate should demonstrate both theoretical expertise and practical experience, and possess:

1. Expertise in multi-cloud orchestration, autonomous scaling, and application slicing.
2. Extensive experience with on-premises clouds and public cloud platforms (e.g., OpenStack, Azure, AWS).
3. Hands-on proficiency in developing automated microservice-oriented ML pipelines (including GPU acceleration) and real-time stream processing using frameworks such as Apache Flink, Apache Kafka, or Spark Streaming, along with resource optimization and performance evaluation.
4. Working knowledge of cloud-native networking and software-defined infrastructure.
5. Excellent communication and collaboration skills for success in multidisciplinary teams.
6. Proficiency in project management and agile methodologies to ensure timely and high-quality research outcomes.

Required Documents

Candidates are requested to submit a comprehensive cover letter that summarizes the research experience of the applicant and a CV.

Please Note: This is a 2 year term position at 40% FTE.

Closing Date:02/25/2025, 11:59PM ET
Employee Group: Research Associate
Appointment Type: Grant - Term
Schedule: Part-Time
Pay Scale Group & Hiring Zone: R01 -- Research Associates (Limited Term) at 40% FTE: $21,029 - $39,430
Job Category: Engineering / Technical