PhD Position on Machine and Transfer Learning: Wageningen University and Research, through the Laboratory of Geo-information Science and Remote Sensing (GRS), invites applications for a fully funded PhD position in the domain of Machine and Transfer Learning under Distribution Shifts for Agricultural Applications and Food Security. This position, part of the AgrifoodTEF project, aims to advance research in modern machine learning methodologies, particularly in deep learning and artificial intelligence, to address challenges related to agricultural applications and food security. The selected candidate will collaborate with an interdisciplinary team of experts in deep learning, remote sensing, crop modeling, and food security.
Designation: PhD Candidate
Research Area: Machine Learning, Transfer Learning, Deep Learning, Agricultural Applications, Food Security, Remote Sensing, Geo-information Science
Location: Wageningen, Netherlands
Eligibility/Qualification:
- M.Sc. degree in artificial intelligence, computer science, environmental or agricultural science, remote sensing, geodesy, or a similar relevant field.
- Demonstrated experience in applied machine learning, preferably in remote sensing or agricultural applications.
- Proficiency in programming in Python and experience in Pytorch, Scikit-Learn, or related modern machine learning libraries.
- Understanding of machine learning principles and concepts.
- High motivation, self-driven curiosity, and enthusiasm to work in a highly dynamic team towards a common objective.
Job Description:
PhD Position on Machine and Transfer Learning under Distribution Shifts for Agricultural Applications and Food Security
The selected candidate will:
- Develop modern machine learning methodologies, with a focus on deep learning, for agricultural applications and food security questions.
- Investigate the development of dynamic deep learning models using multi-model heterogeneous data for crop type classification, crop yield prediction, field boundary delineation, disease and anomaly detection.
- Design and evaluate model architectures for image and time series data, leveraging recent advancements in self-supervised, contrastive, and transfer learning.
- Process and integrate remote sensing and satellite data alongside other relevant data modalities.
- Collaborate with an interdisciplinary and international team within the AgrifoodTEF project.
How to Apply: Interested candidates can respond by clicking here. Please submit a detailed CV, a motivation letter (max 1 page), and a writing sample (max 4 pages demonstrating individual writing skills, e.g., section of a thesis, course assignment, or paper). Additionally, include the names of three references.
Last Date for Apply: 27 December 2023
Disclaimer: This job post is extracted from a reliable source. Applicants are advised to verify details and check for any updates or changes on the official Wageningen University & Research website here for the most accurate and up-to-date information.
