Postdoctoral fellow within AI-Based Analysis of Hyperspectral Images of Tree Rings
About the position
The Faculty of Environmental Sciences and Natural Resource Management (MINA) at Norwegian University of Life Sciences (NMBU) has a vacant three-year post-doctoral position related to the extraction of climate proxy information from hyperspectral images (HSI) of tree rings using artificial intelligence (deep learning).
The candidate to be hired will join our innovative project, titled “Novel Approach to Dendroclimatology Using Hyperspectral Imaging and Deep Learning”. The project is funded by the Human Frontier Science Program. For further details, see https://www.hfsp.org/hfsp-news/2024-research-grants-awardees
The project aims to apply cutting-edge analytical technologies to dendroclimatology, with a specific focus on tropical tree species.
Dendroclimatology, the study of tree rings to extract past climate proxy data, is key to climate research, especially in temperate and boreal regions with clear seasonal patterns. However, in tropical areas, tree ring formation is more complex due to less defined seasons and climate variability. Consequently, climate records from tropical regions, especially in Africa, are scarce, highlighting the need for improved technologies to analyze tree rings and advance the continent’s climate science.
In this project, we aim to pioneer a novel method in dendroclimatology by combining Hyperspectral Imaging (HSI) and Deep Learning (DL) for detecting, characterizing and consequently extracting climate proxy information from tree rings. We will analyze samples from tropical tree species, aiming to advance climate research in tropical regions through the use of these cutting-edge technologies.
If you have a strong interest in climate research and a background in AI, we encourage you to apply.
Main tasks
- Explore advanced deep learning algorithms for processing hyperspectral images of tree rings.
- Explore advanced deep learning algorithms for the detection, characterization, and consequently, the extraction of climate proxy data from hyperspectral images of tree rings.
- Explore advanced statistical methods for developing robust prediction models.
- Writing and publishing scientific papers in peer-reviewed journals.
- Presenting project results in workshops and conferences.
- Collaborating with project members, including a PhD candidate and postdoctoral fellow, in a multidisciplinary environment that integrates hyperspectral imaging, data science, and dendroclimatology labs across Norway and Ethiopia.
Competence
Required Academic qualifications
- A PhD in Data Science, Computer Science, or related fields.
The following experiences and skills will be emphasized:
- Strong background in exploring and developing deep learning models.
- Experience with various types of data analysis, especially image data.
- Proficiency in programming, particularly Python, with a focus on PyTorch and TensorFlow libraries.
- Knowledge of hyperspectral imaging data.
- Proven experience in writing and publishing scientific articles in international peer-reviewed journals.
- Experience working on collaborative academic and research projects.
The following experiences and skills will be considered advantageous:
- Familiarity with basic tree physiology and climate science.
You need to:
- Have strong oral and written communication skills in English, with the ability to collaborate effectively in interdisciplinary and international teams.
- Have proven ability to foster a welcoming, productive work environment and thrive in a multidisciplinary, global setting.
- Have a high work capacity and enthusiasm for teamwork.
Remuneration and further information
The position is placed in government pay scale position code 1352 Postdoctoral Fellow (salary grade 62-77) (NOK 604 900 - 796 600), depending on qualifications. The position follows ordinary meriting regulations.
For further information, please contact Dr. Meley Mekonen Rannestad (project coordinator)
E-mail: [email protected]; phone +4796656375
Application
To apply online for this vacancy, please click on the 'Apply for this job' button above. This will route you to the University's Web Recruitment System, where you will need to register an account (if you have not already) and log in before completing the online application form.
Application deadline: 06.11.2024
Your CV must be entered in JobbNorge's CV form and not just included as an attachment. This is to be able to comply with the regulations of §15 of the Public Administration Act.
Up to ten publications selected by the applicant as most relevant must be attached to the application. If it is difficult to identify the contribution of the applicant in multiple-author publications, a short explanation about the applicant’s part of the work is suggested.
Printed material which cannot be sent electronically should be sent by surface mail to Norwegian University of Life Sciences, Faculty of Environmental Sciences and Natural Resource Management, P.O. Box 5003, NO-1432 Ås, within 06.11.2024. Please quote reference number 24/04898.
Applicants invited for an interview will be asked to present verified copies of diplomas and certificates.
Please note that the report from the expert committee will be sent to all applicants.
About The Faculty of Environmental Sciences and Natural Resource Management
The Faculty of Environmental Sciences and Natural Resource Management (MINA) works with nature and the environment, sustainable use of natural resources, biological and geological processes.
MINA’s employees undertake teaching, research and dissemination within the fields of geology, hydrology and limnology, soil science, environmental chemistry, forestry, ecology, natural resource management, renewable energy, and nature-based tourism.
Our vision is to be a key actor in knowledge production and dissemination, and our goal is to deliver research of high, international quality, and varied and excellent teaching. The faculty’s employees are significant participants within their respective fields of expertise, both nationally and internationally. The faculty is dominated by a vital research culture and high levels of scientific production.
The faculty has about 200 employees, 90 PhD students and 650 students.