Postdoc Efficient Deep Learning for Computer Vision Space App

at  Springer Nature

POSTDOC EFFICIENT DEEP LEARNING FOR COMPUTER VISION SPACE APP

Employer
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg
Location
Luxembourg
Salary
Negotiable
Closing date
4 Jun 2024
Discipline
Computing, Engineering
Job Type
Postdoctoral
Employment - Hours
Full time
Duration
Fixed term
Qualification
Masters
Sector
Academia

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ABOUT THE SNT…

SnT is a leading international research and innovation centre in secure, reliable and trustworthy ICT systems and services. We play an instrumental role in Luxembourg by fueling innovation through research partnerships with industry, boosting R&D investments leading to economic growth, and attracting highly qualified talent.
We’re looking for people driven by excellence, excited about innovation, and looking to make a difference. If this sounds like you, you’ve come to the right place!

We offer an attractive Research Associate position in Computer Vision-based anomaly detection for Space Applications. The successful candidate will join the Computer Vision, machine Intelligence and Imaging (CVI²) research group (http://cvi2.uni.lu) headed by Prof. Djamila Aouada. CVI² offers the opportunity to work on multiple national and international academic projects and in collaboration with industrial partners. All projects require conducting full scale experiments, including real-time implementation, data acquisition, training, and validation at the SnT Computer Vision Lab (https://youtu.be/knn24Tnv_NA) and the SnT Zero-G Lab (https://youtu.be/knEjlkSit2Y). This is a fully funded Postdoc position for 1 to 2 years (possible extension(s) up to 5 years in total). The position holder will be involved in different projects on efficient deep Learning for Computer Vision space applications and will be closely collaborating with industrial partners. He/she will be required to perform the following tasks:

• Shaping research directions and producing results. Topics include but are not limited to:
• Deep learning models suitable for deployment on edge devices (e.g., NVIDIA Jetson, FPGA, etc.)
• Neural Architecture Search (NAS) for minimal deep architectural design
• Efficient in-orbit object pose estimation and tracking
• Disseminating results through scientific publications
• Providing guidance to PhD and MSc students
• Providing support in setting up and running experiments in the SnT Computer Vision and the Zero-G labs
• Participating in organizing relevant workshops and demonstrations
• Participating to teaching activities
• Coordinating research projects and delivering outputs
• Working closely with project’s stakeholders
• Collaborating with industrial partners