Computer vision and application aware compression for phenotyping
1 Research Collaborator position
- Image and video compression, image analysis, pattern recognition, machine learning, data mining, embedded systems
- We have funding from the EU to investigate affordable imaging sensors and distributed analysis frameworks for plant phenotyping. Our proposed framework involves the development of imaging sensors based on low cost ARM devices as well as software solutions that employ image and video compression (and particularly application aware compression) to minimize the necessary bandwidth. We are looking for an enthusiastic and strongly motivated junior researcher to join our lab in Italy and propose innovative approaches to the above problems. The research collaborator is expected to focus mostly on research
- Formal requirements:
- Possession of a 4 or 5-year degree or equivalent MSc's level education in Computer Science, Information Engineering, or Electrical Engineering. Excellent knowledge of English, both written and spoken.
- Specific requirements:
- Candidates should be near the completion of a Ph.D. in the intersection of image and video compression with computer vision and machine learning. A good record of international publications demonstrating prior experience is required. Experience with embedded devices, application aware compression, and distributed computing on the cloud, are considered a plus. Experience in image based plant phenotyping problems is considered a plus. The candidate should have good programming skills and a good mathematical background.
- Research Area:
- Computer Science and Applications
- Research Unit:
- PRIAn - Pattern Recognition and Image Analysis
- Type of contract:
- Assegno di ricerca
- Gross remuneration:
- €25.000,00 in total/year
- 1 year not renewable
Apply ONLINE only.
Before starting prepare the application attachments and information as listed below.
- Personal info and contact info (compulsory)
- Number of your Identity Document (Passport or Identity Card) (compulsory)
- University degree and ongoing PhD (compulsory)
- Your CV in English (compulsory)