PhD Researcher in Adaptive UAV Sensing Techniques for Flood Crisis Management


Job description 

The IDLab Ghent research group is seeking a highly motivated and talented PhD student to join the distributed machine learning team. Our team focuses on developing efficient machine learning algorithms for perception and control; with diverse applications in industry, robotics, remote sensing and agriculture. 


Due to climate change, an increase in the magnitude and frequency of extreme precipitation events is expected, which may lead to more intense and frequent river flooding. When a flood disaster hits, situational awareness on victim location, floodwater presence and road accessibility is key to ensure targeted and successful rescue operations. UAV systems are a promising source of information, since they offer a synoptic view even under cloud cover and do not rely on fixed infrastructure. However, their full exploitation as a source of information is hampered by battery-induced flight time limitations.  

The objective of this PhD is to develop deep learning based computer vision pipelines for on-board processing of RGB data. Since UAVs used in disaster management capture high-resolution images, a form of self-attention mechanism will be needed for selecting relevant image regions to be processed in more detail. This selection mechanism will be informed by the flight plan, semantic interpretation of the observed scene and post-processing tasks on key frames that are transmitted to the ground operator or a cloud-based image processing task (e.g. for georeferencing).  

Role and responsibilities

This a joint PhD project between Ghent University and VITO, allowing you to interact with fellow researchers in resource-efficient ML and experts in remote sensing technologies. The project is situated within a collaborative project involving Belgian and German stakeholders. You will be able to analyze and process on already available UAV data captured during recent flooding events.

Your main tasks include: 

  • Reviewing literature on efficient deep neural network methods, foundation models and geographic techniques such as georeferencing
  • Designing and implementing state-of-the-art transformer-based algorithms for real-time processing of high-resolution UAV imagery
  • Researching and prototyping adaptive computation techniques that process informative image regions in more detail
  • Researching and prototyping mechanisms to select the most informative frames to be transmitted to a ground station for further post-processing
  • Writing high quality publications, targeting top journals and international conferences.

In addition to your primary research responsibilities, you will actively contribute to the educational mission of our institution by providing support for various courses in areas such as machine learning and algorithm design. In addition, you can take on a mentoring role by supervising master theses related to the subject of this PhD.  

Job profile 

We are looking for a highly creative and motivated PhD student with the following qualifications and skills: 

  • You have (or will obtain in the next months) a (European) master's degree in computer science, Artificial Intelligence, or equivalent, with excellent ('honors'-level) grades. Your degree must be equivalent to 5 years of studies (bachelor + master) in the European Union.
  • You have a strong background in machine learning and are eager to advance the state-of-the-art in adaptive inference.
  • You are willing to engage in novel algorithmic design, going beyond the mere application of existing techniques.
  • You are willing to study geographic computer vision techniques to the extent needed to realize the PhD tasks.
  • You have excellent computer science skills (python, git, linux, etc.).
  • You have hands-on experience with machine learning frameworks such as PyTorch. 
  • Experience with computer vision, remote sensing, drones, or embedded compute platforms (e.g. NVIDIA Jetson, Edge TPU, …) is considered a strong plus.
  • You have strong analytical skills to interpret the obtained research results.
  • You are a team player and have strong communication skills.
  • Your English is fluent (C1 CEFR level), both speaking and writing.

Our offer

We offer the opportunity to do this research in an international and stimulating environment. The research will be mainly conducted at the premises of IDLab, located in Ghent. However, there will be frequent interactions with researchers at VITO in Mol.

 Ghent University consistently ranks among the best 100 universities in the world. Located in the heart of Europe, Ghent is a beautiful and welcoming city with plenty of cultural and leisure activities. 

VITO is a leading European research organization in the area of cleantech and sustainable development, with a team of 100+ employees working on remote sensing. As the successful candidate you will be welcomed as a member of the VITO Remote Sensing team and supervised by dr. Lisa Landuyt. You will get in touch with application experts and stakeholders that are ready to put your research into practice.

The selected candidate will be offered a 4-year employment, with an intermediate evaluation after the first year (1+3 years). The salary is competitive and will be determined by the university salary scales. In addition, staff members can count on a number of benefits, such as a broad range of training and educational opportunities, 36 days of vacation leave, bicycle allowance, and more.

How to apply 

Send your application by email to Prof. Pieter Simoens and dr. Sam Leroux.

Applications should include:

  • An academic/professional resume
  • Transcripts of study results
  • A short overview describing your earlier research or technical work (e.g., scientific papers, link to GitHub repository, master thesis, report on project work, etc.). These documents need not be on the topic of the advertised position.

After a first screening, selected candidates will be invited for an interview (also possible via Teams).  The selection process will involve multiple steps.

Application deadline: continuous evaluation until the vacancy is filled.