Job Details
Responsibilities- Teaching Topics in Computer Vision for Aquaculture: Preparation of lectures and practical sessions for a new specialization course that includes computer vision for aquaculture. This includes development of teaching material such as lecture notes/compendium and exercises.
- Research and project proposals: Contribute to research initiatives/project proposals that includes computer vision for aquaculture, in areas such as individual salmon recognition, fish net inspection, and 3D reconstruction for aquaculture and potential to the development of autonomous marine solutions, with the aim to submit at least one RCN project proposal within the period.
- Student Supervision: Propose topics for, and supervise project and master’s students working on computer vision applications in aquaculture.
- PhD and Research Supervision: Guide PhD candidates, postdoctoral fellows, researchers, and students at the department who wish to require expertise in computer vision for aquaculture.
- Community Building: Contribute to building a strong academic community focused on computer vision for aquaculture within the department, fostering collaboration with other departments on related methodologies and applications.
Requirements
The position of Assistant Professor within “Advanced Computer Vision for Aquaculture Cybernetics” requires that you meet the criteria in the Regulations concerning appointment and promotion to teaching and research posts section 1-4.- Your academic work must include work in practical and/or theoretical computer vision for aquaculture
- A PhD in computer science, electrical engineering, robotics, or a related field with a strong focus on computer vision
- At least 2 years of research experience after PhD in computer vision with applications in aquaculture or marine environments
- A robust publication record in peer-reviewed conferences and journals, demonstrating expertise and leadership in computer vision and its applications in aquaculture
- Proven ability to teach and mentor students at undergraduate and graduate levels, with experience in curriculum development and course design
- Proficiency in programming languages such as C++, Python, and MATLAB, and experience with deep learning frameworks (e.g., TensorFlow, PyTorch) and computer vision libraries (e.g., OpenCV)
- Demonstrated experience in collaborating with interdisciplinary teams, including marine biologists, aquaculture engineers, and industry partners
- Ability to participate in and initiate project grant writing and management
- Excellent written and verbal communication skills in English, with the ability to convey complex technical concepts to students, colleagues, and non-specialist audiences
- Leadership skills with a proactive approach to mentoring junior researchers for a collaborative and supportive research environment
- Development in teaching and counselling of students over time
- Extensive experience with supervision preferably at the master / PhD level
- Participation in the development of educational quality in a work environment
Preferred Selection Criteria
- Applicants with teaching experience from higher education will be preferred
- Experience, in developing and implementing computer vision solutions suitable for the aquaculture industry or marine technology
- Application-driven experience within aquaculture operations
- Communication Skills: Strong communication skills in English, both oral and written, with students and colleagues
- Collaboration Skills: Excellent teamwork and collaboration abilities
- Flexible Mindset: Flexibility to work on both fundamental and applied research
- Structured Approach: Ability to work in a structured manner
- Solution and Result Orientation: Solution- and result-oriented mindset
Contact Information
Norwegian University of Science and Technology (NTNU)https://www.jobbnorge.no/en/available-jobs/job/265054/adjunct-associate-professor-within-advanced-computer-vision-and-ai-for-aquaculture-cybernetics
https://www.jobbnorge.no/jobseeker/#/application/apply/265054
Post Date: 10/23/2024 2:26:50 PM
Closing Date: 11/13/2024 12:00:00 AM