PhD student in Computing Science with focus on serverless edge AI

Umeå University, Faculty of Science and Technology

Umeå University is one of Sweden’s largest higher education institutions with over 37,000 students and about 4,700 employees. The University offers a diversity of high-quality education and world-leading research in several fields. Notably, the groundbreaking discovery of the CRISPR-Cas9 gene-editing tool, which was awarded the Nobel Prize in Chemistry, was made here. At Umeå University, everything is close. Our cohesive campuses make it easy to meet, work together and exchange knowledge, which promotes a dynamic and open culture.

The ongoing societal transformation and large green investments in northern Sweden create enormous opportunities and complex challenges. For Umeå University, conducting research about – and in the middle of – a society in transition is key. We also take pride in delivering education to enable regions to expand quickly and sustainably. In fact, the future is made here.

Are you interested in learning more? Read about Umeå university as a workplace

Department of computing science

The Department of Computer Science, characterized by world-leading research in several scientific fields and a multitude of educations ranked highly in international comparison, is looking for a Doctoral student in serverless edge AI.

The Department of Computing science has been growing rapidly in recent years where focus on an inclusive and bottom-up driven environment are key elements in our sustainable growth. The 60 Doctoral students within the department consists of a diverse group from different nationalities, backgrounds, and fields. If you work as a Doctoral student with us you will receive the benefits of support in career development, networking, administrative and technical support functions along with good employment conditions.

See more information at:
https://www.umu.se/en/department-of-computing-science/

Project description

Serverless edge AI is a transformative shift in computing architecture, merging serverless edge computing and artificial intelligence (AI) to harness and abstract the distributed cloud-edge resources. Unlike traditional serverless solutions, such as Function as a Service (FaaS) technology, which struggle with real-time adaptability, efficient function placement, and cost management, serverless edge AI allows AI models to be deployed closer to data sources, enhancing performance with lower latency, faster response times, scalability, and stronger privacy controls. This approach abstracts infrastructure management, enabling real-time AI processing directly where data is generated or consumed, meeting critical demands for reliability, privacy, and energy efficiency.

This project aims to investigate and develop a serverless edge AI framework that addresses key challenges, such as enabling learning and inference at the edge for efficient resource orchestration, dynamically relocating workloads, supporting FaaS application mobility, and forecasting energy use and workload distribution across the cloud-edge continuum. By building an optimized framework with advanced algorithms (e.g., deep neural networks, sequence modeling, and multi-objective optimization), this project will enhance edge-based learning and decision-making, allowing for efficient orchestration of distributed resources. Key functionalities include intelligent serverless function execution, smart edge cluster provisioning, on-demand workload relocation, and device-specific model personalization. The framework and algorithms will be rigorously tested in real-time and simulation environments, demonstrating notable advantages over traditional centralized cloud models.

The successful candidate will contribute to the Autonomous Distributed Systems (ADS) Lab within the Department of Computing Science. The ADS Lab is an internationally leading research group with a focus from distributed AI to autonomous resource management and modern. The Lab currently comprises over 20 experienced and world-leading research colleagues from more than 10 different countries. For more information, see www.cloudresearch.org

This position is part of an EU-funded project called SovereignEdge COGNIT - a large new research initiative to build a next-generation European Edge-Cloud Framework (https://cognit.sovereignedge.eu/).

Admission requirements

The general admission requirements for doctoral studies are a second-cycle level degree or completed course requirements of at least 240 ECTS credits, of which at least 60 ECTS credits are at second-cycle level, or have an equivalent education from abroad or equivalent qualifications. To fulfil the specific entry requirements for doctoral studies in computing science, the applicant is required to have completed at least 90 ECTS credits in computing science. Applicants who otherwise have acquired skills that are deemed equivalent are also eligible.

Candidates are expected to have solid foundations in the theory and algorithms of project related areas, such as machine learning, edge computing, distributed systems, and excellent programming ability. Experience in broad competence areas, including development of machine learning algorithms, statistical analysis methods, distributed learning, and discrete optimization is desirable. Additionally, experience in software development, configuring experimental testbeds, and developing simulations is a merit. A strong command in both written and spoken English language is a key requirement.

Besides creativity and a curious mind, important personal qualities include the ability to work independently as well as together with others either in a group or outside. You are also expected to have a willingness to develop yourself continuously to become a competent researcher.

About the position

The position provides you with the opportunity to pursue PhD studies in Computing Science for four years, with the goal of achieving the degree of Doctor in Computing Science. While the position is mainly devoted to spend 100 % time on PhD studies.

The procedure for recruitment for the position is in accordance with the Higher Education Ordinance (chapter 12, 2§) and the decision regarding the position cannot be appealed.

The expected starting date is February 1, 2025 or as otherwise agreed.

Application

Applications must be submitted electronically using the e-recruitment system of Umeå University.

A complete application should contain the following documents:

A cover letter including a description of your research interests, your reasons to apply for the position, and your contact information. Generic cover letters, or applications without cover letter will not be considered.
A curriculum vitae.
Reprints / copies of completed BSc and/or MSc theses and other relevant publications, if any.
Copies of degree certificates, including documentation of completed academic courses and obtained grades.
Contact information for three reference persons.
Documentation and description of other relevant experiences or competences.

The application must be written in English or Swedish. Attached documents must be in pdf format. Applications must be submitted electronically using the e-recruitment system of Umeå University, and be received no later than 2024-12-02

Selected applicants will be invited for an interview round, including a computing and programming assignment.

Umeå University wants to offer an equal environment where open dialogue between people with different backgrounds and perspectives lay the foundation for learning, creativity and development. We welcome people with different gender, backgrounds and experiences to apply for the current employment.

For additional information, please contact Assist. Prof. Monowar Bhuyan (monowar@cs.umu.se) or Prof. Erik Elmroth (elmroth@cs.umu.se)

We look forward to receiving your application!