Master Thesis “Deep Learning for Scalable Phenotyping Using UAV-Acquired Mu

at  Austrian Institute of Technology

As Austria’s largest research and technology organisation for applied research, we are dedicated to make substantial contributions to solving the major challenges of our time, climate change and difitalisation. To achieve our goals, we rely on our specific research, development and technology competencies, which are the basis of our commitment to excellence in all areas. With our open culture of innovation and our motivated, international teams, we are working to position AIT as Austria’s leading research institution at the highest international level and to make a positive contribution to the economy and society.
Our Center for Vision, Automation & Control located in Vienna invites applications for a master’s thesis position. At the Center for Vision, Automation & Control our research unit “Assistive & Autonomous Systems” closely works with industrial partners in order to develop safe and reliable technology components for assistance systems, that can be used in various areas of applications, for e.g.: Railway / Public Transport, Construction Industry, Logistics & Transportation, Agriculture & Forestry as well as in Aviation.
Our research focuses on developing advanced deep learning frameworks to predict plant phenotypes, such as “yield potential” and “disease severity”, in winter wheat using UAV-acquired multispectral data. Our aim is to build a scalable framework that leverages time-series multispectral images for efficient and accurate phenotype prediction

CENTER FOR VISION, AUTOMATION & CONTROL

• Throughout this master’s thesis, you will closely work with our researchers and engineers, who focus on the development and deployment of assistive and autonomous systems, particularly unmanned aircrafts and land-based mobile machines – Hereby you will communicate and engage with a multi-disciplinary team to solve diverse multi-facetted challenges.
• Under the guidance of our experts, you will contribute to a rapidly growing field in aerial robotics.
• You will investigate the state-of-the-art and apply it in real-world scenarios and – as a final step – will design a proof-of-concept demonstration.
• You design, develop, and optimise deep learning models for processing time-agnostic multispectral image data.
• You adapt and benchmark our model to improve the prediction of winter wheat phenotypes.
• You preprocess and manage large-scale UAV-acquired multispectral datasets.
• You collaborate with an interdisciplinary team of researchers in a supportive and dynamic environment.
• You participate in interpreting model predictions in real-world agricultural applications.
• You conduct in-depth analysis and reporting of results, focusing on model interpretability and performance.

Your qualifications as an Ingenious Partner:

• Current enrollment in a Master’s programme in Computer Science, Geoinformatics, Remote Sensing, Mathematics, Statistics, Physics, Engineering, or a related STEM field.
• Strong foundation in machine learning, with experience in deep learning frameworks (e.g., PyTorch, TensorFlow).
• Familiarity with computer vision techniques, especially convolutional neural networks and attention mechanisms.
• Ability to work both independently and as part of a collaborative research team.
• Good knowledge of verbal and written English

What to expect:

• Duration of the master’s thesis project: 6 months
• Start date: ideally 01.12.2024 – However there is some flexibility concerning the start-date
• EUR 968 gross per month for 20 hours/week based on the collective agreement. There will be additional company benefits. As a research institution, we are familiar with the supervision and execution of master theses, and we are looking forward to supporting you accordingly!

At AIT diversity and inclusion are of great importance. This is why we strive to inspire women to join our teams in the field of technology. We welcome applications from women, who will be given preference in case of equal qualifications after taking into account all relevant facts and circumstances of all applications.
Please submit your application documents including your CV, cover letter, relevant certificates (transcript of records) online:

Please refer the Job description for details