Environmental AI Technician, Biological Physics Theory Unit
We are seeking a talented deep-learning/machine-learning engineer to work on environmental acoustic AI. The successful candidate will develop optimized, scalable deep-learning models for the detection and classification of highly invasive anuran species, using audio data recorded, in the first instance, with passive acoustic monitoring sensors.
Why Join Us
1. Be part of a pioneering team addressing critical environmental challenges through modern AI
2. Enjoy the unique and inspiring setting of Okinawa while advancing your own career
3. Get access to a wide range of professional development opportunities, apply for patents (if interested), and network with a diverse scientific community
The Okinawa Institute of Science and Technology Graduate University was established in 2011 to contribute to the development of science and technology worldwide and to serve as a hub of innovation in Okinawa. OIST is a dynamic new graduate university of science and technology in Okinawa Prefecture, Japan which offers a 5-year PhD program and brings together outstanding researchers from across the country and across disciplines to conduct cutting-edge scientific research.
The university is located on 85 hectares of protected forestland overlooking a beautiful shoreline and coral reefs. The campus is striking architecturally, and the facilities are outstanding. To facilitate multidisciplinary research, there are no academic departments. Outstanding resources and equipment are provided and managed to encourage easy access and collaboration.
English is the official language of the University, and the university research community is fully international, with more than 50 countries represented. OIST is rapidly gaining recognition in the worldwide academic community as a model for excellence in education and research, and our unwavering commitment to scientific and technological innovation is dedicated to generating progress that will fuel Okinawa's economic growth.
1. Develop deep-learning models for acoustic classification of alien invasive anuran species
2. Employ state-of-the-art methods, such as generative AI and hyper-parameter tuning, to enhance model performance and scalability
3. Collaborate with a multidisciplinary team to integrate models into larger environmental AI projects
4. Maintain proper documentation in our centralized Wiki
5. Provide regular progress reports and dissemination of research results
(Required)
1. MSc or PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related quantitative field
2. Strong understanding of machine-learning/deep-learning, including convolutional neural networks
3. Working knowledge of software development principles, Python programming, and associated ML frameworks (Tensorflow, PyTorch)
4. Excellent teamwork and communication skills
(Preferred)
The below are desirable, but for a suitable candidate they could also be developed as part of the project:
1. Knowledge of modern deep-learning architectures, such as transformers/generative AI.
2. Experience with acoustic classification models (e.g., BirdNet [1])
3. Knowledge of Edge AI Engineering
[1] Kahl, S., Wood, C. M., Eibl, M., & Klinck, H. (2021). BirdNET: A deep learning solution for avian diversity monitoring. Ecological Informatics, 61, 101236.
In accordance with the OIST Employee Compensation Regulations
Expected Annual Salary: 4.5M – 6.0Myen (Technician II to III level with MSc degree)
*This amount represents the expected annual salary range for this position, which will be determined based on the candite’s experience, skills and background in accordance with the OIST Employee Compensation Regulations.
Benefits:
- Relocation, housing and commuting allowances
- Annual paid leave and summer holidays
- Health insurance (Private School Mutual Aid)
- Welfare pension insurance (kousei-nenkin)
- Worker's accident compensation insurance (roudousha-saigai-hoshou-hoken)
- Access to Child Development Center
- Access to Schooling Options
- Language Education
- Resource Center (Daily Life Support in Okinawa)
- Remote Work system
• Cover letter (English)
• Curriculum vitae (English)
• Names and contact information of 2-3 referees, one of which should be a previous employer
* Please be sure to indicate where you first saw the job advertisement.
* Prior to the start of employment all new hires are required to successfully complete a background check. Personal information including employment history and academic background should be submitted to OIST after a conditional offer of employment.
How to Apply
Apply by submitting your application documents to Dr. Kosmas Deligkaris:
kosmas.deligkaris[at]oist.jp (Please replace [at] with @ before using this email address)
* OIST Graduate University is an equal opportunity, affirmative action educator and employer and is committed to increasing the diversity of its faculty, students and staff.
* Information provided by applicants or references will be kept confidential, documents will not be returned. All applicants will be notified regarding the status of their applications. OIST Privacy Policy
* Recruiting Organization: Okinawa Institute of Science and Technology School Corporation
* Prevention of Passive Smoking: No smoking indoors
* Please view our policy for rules on external professional activities
(Information Disclosure – 10. Others – 6. OIST Rules for Concurrent Appointment).
* Further details about the University can be viewed on our website
Full-time, fixed-term appointment for 1 year. Contract initially with 6-month probationary period (inclusive). This contract may be renewed by taking into consideration the performance, conduct, and behavior of the Employee and OIST’s financial and other circumstances.
9:00-17:30 (Discretionary)
Holidays
Saturday, Sunday, National holidays, and Year-end and New Year holidays (Dec. 29 – Jan. 3)
Leave
Annual Paid Leave, Summer Leave, Sick Leave, and Special Leave
Overtime work hours
Discretionary Working Hours System：
In the discretionary labor system for specialized work, the employee shall be deemed to have worked 7 hours 30 minutes per day.
We thank all those who apply but only those selected for further consideration will be contacted.