Open Postdoc position on Neuromorphic vision for dones

Job description

Neuromorphic sensing and processing form a highly promising technology for creating autonomous small drones, due to the potential for high-speed perception at a low energy cost. The goal of this research project is to develop neuromorphic vision algorithms for complex visual tasks that are relevant to drones, such as optical flow determination, ego-motion estimation, and depth perception. Specifically, we will investigate unsupervised [1] and self-supervised learning algorithms [2] for spiking neural networks (SNNs) with as ultimate goal to implement them onboard of our drone in the context of a control task. In the research we will expand upon our previous work on unsupervised learning of optical flow with a spiking neural network [1], self-supervised learning (e.g., [2,3,4]), and transferring learned SNNs to controlling a real drone in flight [5].

The project will be carried out within the Control and Simulation section of the Faculty of Aerospace Engineering. A large component will consist of developing self-supervised neuromorphic algorithms applied to event-based camera data, typically performed with the help of a scripting language like Python. However, during the development of the algorithms, the final application on real onboard neuromorphic hardware has to be taken into account, limiting the allowed complexity of the networks. Successful algorithms will then be ported to a real drone. Although this latter part will be a team effort, it will be highly appreciated if the postdoc can play an important role also in the robotic implementation of the algorithms. The postdoc’s supervisor will be prof. dr. Guido de Croon from the Micro Air Vehicle Laboratory.

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We are looking for a candidate with a PhD degree in an area such as Artificial Intelligence, Computer Science, Robotics, Aerospace Engineering, or a similar field. The candidate is expected to be passionate about developing novel AI algorithms with an application on real robotic hardware (“AI at the edge”). Programming experience is required in Python/MATLAB and/or C/C++.

Previous experience with deep learning algorithms or neuromorphic sensing and processing is an asset. Furthermore, experience with developing robotic systems, working with both their hardware and software, is highly appreciated. Please note that the postdoc will work within a team with ample experience in design and prototyping of tiny drones, autopilots (in particular Paparazzi) and micro-electronics as well as with bio-inspired artificial intelligence and control. In particular, the post doc will play a senior role in a team of multiple PhD students that work on neuromorphic sensing and processing. The candidate must have strong analytical skills and must be able to work at the intersection of the research domains of artificial intelligence and robotics. A very good command of the English language is required, as well as excellent interpersonal and communication skills.


Conditions of employment

Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities. The TU Delft offers a customisable compensation package, a discount on health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged.

For international applicants we offer the Coming to Delft Service and Partner Career Advice to assist you with your relocation. An International Children’s Centre offers childcare and there is an international primary school.


TU Delft (Delft University of Technology)

Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context. At TU Delft we embrace diversity and aim to be as inclusive as possible (see our Code of Conduct). Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale.

Challenge. Change. Impact! 


Faculty Aerospace Engineering

The Faculty of Aerospace Engineering at Delft University of Technology is one of the world’s most highly ranked (and most comprehensive) research, education and innovation communities devoted entirely to aerospace engineering. More than 200 science staff, around 250 PhD candidates and over 2,700 BSc and MSc students apply aerospace engineering disciplines to address the global societal challenges that threaten us today, climate change without doubt being the most important. Our focal subjects: sustainable aerospace, big data and artificial intelligence, bio-inspired engineering and smart instruments and systems. Working at the faculty means working together. With partners in other faculties, knowledge institutes, governments and industry, both aerospace and non-aerospace. Working in field labs and innovation hubs on our university campus and beyond.

Click here to go to the website of the Faculty of Aerospace Engineering.


Additional information

Include a cover letter along with a detailed curriculum vitae, a separate motivation letter stating why the proposed research topic interests you, electronic links to or copies of a few selected publications (if applicable), a short summary of your PhD thesis research, names and addresses of two to three reference persons, and other information that might be relevant to your application. Job interviews will take place mid December, 2021.

For information about this vacancy, you can contact the C&S secretary B.M. Markus.



[1] Paredes-Vallés, F., Scheper, K. Y., & de Croon, G. C. H. E. (2019). Unsupervised learning of a hierarchical spiking neural network for optical flow estimation: From events to global motion perception. IEEE transactions on pattern analysis and machine intelligence, 42(8), 2051-2064.

[2] Paredes-Vallés, F., Hagenaars, J., & de Croon, G. (2021). Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks. arXiv preprint arXiv:2106.01862.

[3] de Croon, G. C. H. E., De Wagter, C., & Seidl, T. (2021). Enhancing optical-flow-based control by learning visual appearance cues for flying robots. Nature Machine Intelligence, 3(1), 33-41.

[4] Ho, H. W., De Wagter, C., Remes, B. D. W., & de Croon, G. C. H. E. (2015). Optical flow for self-supervised learning of obstacle appearance. In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3098-3104). IEEE.

[5] Dupeyroux, J., Hagenaars, J., Paredes-Vallés, F., & de Croon, G. C. H. E. (accepted at ICRA 2021). Neuromorphic control for optic-flow-based landings of MAVs using the Loihi processor.


Application procedure

Are you interested in this vacancy? Please apply before 01-12-2021 via the application button and upload your motivation and CV.

  • A pre-employment screening can be part of the selection procedure.
  • Applying for an exemption for specific research and educational areas is an obligatory part of the selection procedure for this vacancy. This exemption must be obtained from the Ministry of Education, Culture and Science (OCW) before an employment contract is agreed upon. Click here for more information.
  • You can apply online. We will not process applications sent by email and/or post.
  • Acquisition in response to this vacancy is not appreciated.

Apply online here.