Department of Pharmacy, University of Copenhagen · 6 hours ago
PhD fellowships in Remote Sensing and Deep Learning for understanding woody ecosystem response to climate change
United States
Full-time
Remote
New GradThe Department of Geosciences and Natural Resource Management at the University of Copenhagen is inviting applications for two PhD fellowships focused on Remote Sensing and Deep Learning to understand woody ecosystem responses to climate change. The PhD positions will involve developing research techniques to characterize changes in global woody ecosystem functioning and structure, contributing to the understanding of how climate change impacts these ecosystems.
Research
Responsibilities
Carry through an independent research project under supervision
Complete PhD courses corresponding to approx. 30 ECTS / ½ FTE
Participate in active research environments, including a stay at another research institution, preferably abroad
Teaching and knowledge dissemination activities
Write scientific papers aimed at high-impact journals
Write and defend a PhD thesis on the basis of your project
Qualification
Required
Fluency in spoken and written English is a requirement
Python programming skills
Relevant experience in remote sensing and deep learning
Experience with handling and processing large image datasets
Relevant publications
Relevant work experience
Other relevant professional activities
Curious mind-set with a strong interest in how remote sensing and AI can be used to assist in reaching the global sustainable development goals like SDG 13 (Climate Action) and 15 (Life on Land)
Good language skills
Professional qualifications relevant to the PhD project
Preferred
Prior experience with remote sensing of tree resources is considered an advantage but is not formal a requirement
Company
Department of Pharmacy, University of Copenhagen
Research, education and innovation within the field of Pharmaceutical Sciences.
Founded in 1892
51-200 employees
Funding
Current Stage
Growth StageCompany data provided by crunchbase