The Department of Applied Physics and Electronics at Umeå University has an immediate opening for a postdoctoral position for the project Machine-Learning in Real-Time Safety-Critical Systems.
No. of Positions: 1
Research Field: Computer Science, Machine learning
Deadline to Apply: August 16, 2021 (GMT +2)
Joining Date: Nov 01, 2021
Contract Period: 2 Years
Salary: According to Standard Norms
A person who has been awarded a doctorate or a foreign qualification deemed to be the equivalent of a doctorate in Electrical Engineering or Computer Science qualifies as a postdoctoral fellow. Priority should be given to candidates who have completed their doctoral degree no more than three years before the closing date of the application. A candidate who has completed their degree prior to this may be considered if special circumstances exist. Special circumstances include absence due to illness, parental leave or clinical practice, appointments of trust in trade unions or similar circumstances.
The applicant should have a strong background in Machine Learning, especially Deep Learning and Reinforcement Learning. Very good skills in written and oral English are required as is good ability to act both individually as in a team. Creativity and ability to take initiatives are also required.
Knowledge of real-time systems and safety-critical/autonomous systems is meritorious.
According to the lab interest
How to Apply?
Online Application through "Apply Now" Button from this page or from advertisement webpage (URL below)Reference Number: -AN 2.2.1-1005-21
(If any, use it in the necessary place)
A complete application includes:
- Personal letter with maximum of 2 pages,
- Curriculum vitae CV with publication list,
- Certified copy of doctoral degree certificate,
- Certified copy of doctoral thesis,
- Contact information for at least two reference persons,
- Other documents that you wish to claim.
The application, including attached documents, must be written in English or Swedish. The application is made through our electronic recruitment system. Documents sent electronically must be in Word or PDF format. Log in to the system and apply via the button at the end of this page. The closing date is Aug 16, 2021. (“Certified” means that a person (who can be identified) can guarantee the authenticity of the certificate.)
About the Department of Applied Physics and Electronics
The Department of Applied Physics and Electronics currently has around 90 employees and conducts research in fields such as Energy Engineering, Laser Spectroscopy, Biomedical Engineering and Electronics and System Engineering. For more information, see https://www.umu.se/en/department-of-applied-physics-and-electronics/research/.
Project description and working tasks
Machine Learning (ML) components, such as Deep Neural Networks (DNNs), are deployed extensively in one or more of the processing stages (perception/planning/control) in today’s safety-critical autonomous systems such as Autonomous Vehicles. Large DNN models can achieve higher accuracy for complex tasks, but also demand more computing power and memory size. Real-Time Embedded (RTE) systems in such autonomous systems typically have limited hardware resources in terms of CPU speed, memory size and network bandwidth. ML components must be deployed in such systems while meeting real-time constraints. This project aims to develop techniques and tools for multi-objective design optimization and tradeoff analysis of ML components in resource-constrained safety-critical systems, leveraging techniques such as model compression to reduce model complexity, and exploring tradeoffs among multiple conflicting objectives of accuracy, computational efficiency, formal verification efficiency and robustness. The concrete working tasks will be defined taking into consideration of the candidate’s research background and interests, as long as they are within project scope and can generate high-quality publications.
About the Employer: Umeå University (UMU)
Further details are provided by Zonghua Gu (firstname.lastname@example.org) and Thomas Olofsson (head of department) (email@example.com)
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