Research Associate in Efficient Deep Learning for Computer Vision Space Applications | Luxembourg City, Luxembourg | University of Luxembourg

Position:	Research Associate in Efficient Deep Learning for Computer Vision Space Applications
Institution:	University of Luxembourg
Location:	Luxembourg City, Luxembourg
Duties:	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
Requirements:	A PhD degree in Electrical Engineering, Computer Science, Applied Mathematics or a related field; Competitive research record in Computer Vision, preferably with publications at top-tier CV/ML conferences/journals (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, TPAMI, etc.); Broad experience with machine learning algorithms and deep learning concepts; Highly experienced in one or more of the following topics: Efficient deep learning; Neural Architecture Search (NAS); Embedded systems; Jetson-Nano and/or FPGA deployment; Object pose estimation and tracking

Text:	Research Associate in Efficient Deep Learning for Computer Vision Space Applications 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 A PhD degree in Electrical Engineering, Computer Science, Applied Mathematics or a related field; Competitive research record in Computer Vision, preferably with publications at top-tier CV/ML conferences/journals (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, TPAMI, etc.); Broad experience with machine learning algorithms and deep learning concepts; Highly experienced in one or more of the following topics: Efficient deep learning; Neural Architecture Search (NAS); Embedded systems; Jetson-Nano and/or FPGA deployment; Object pose estimation and tracking