Multiple PhD and postdoc positions in Computer Vision and Machine Learning

Eindhoven University of Technology is home to the Mobile Perception Systems lab that researches computer vision and machine learning to improve perception technology of mobile autonomous systems. The MPS lab has a strong background in scene parsing methods such as semantic and panoptic segmentation and vision-based motion and collision prediction methods. In total, the MPS lab has 5 fully funded PhD and/or postdoc positions covering specific topics but there is also the opportunity to define your research.

Our research positions:
We are looking for highly motivated candidates to work on any of the following topics (when applying for a position, you can choose to focus your application letter on one of them) :

1. Multi-modal self-supervised learning for vision and radar-based collision risk prediction
   1. Due to the complexity of collision risk prediction of an autonomous vehicle in urban conditions, and the many edge cases that can be encountered, it is practically impossible to gather comprehensive enough training data. The solution space that will be explored consists of a combination of i) learning from auxiliary simulated tasks, ii) domain randomization and iii) domain invariant/resilient deep representations.
2. Multi-objective neural architecture search for resource-constrained inference
   1. Considering Multi-objective Neural Architecture Search, the research will explore (i) how to define a radar-targeted DNN search space, (ii) how to efficiently explore that space while not being overly constrained to straightforward solutions, (iii) which hardware-aware metrics are realistic, relevant, and feasible to evaluate and optimize.
3. Explainable inference for safety-critical decision-making by autonomous vehicles
   1. This research requires rigorous analysis of the concepts 'explainable', 'interpretable', and 'transparent' and relating these concepts to the specific requirements of stakeholders in autonomous driving. On this basis, it is possible to derive concrete technical requirements and to research and improve state-of-the-art techniques from Explainable AI, such as saliency mapping, counterfactual explanation, and linear approximation, to satisfy the requirements.
4. Self-supervised continual learning at the edge
   1. Many AI systems are deployed at the edge, e.g. in an autonomous vehicle or robots, and require updating their off-line trained models according to experiences in the environment. This research focuses on self-supervised training techniques, such as those used in state-of-the-art unsupervised domain adaption, to continuously update and improve the model from experiences in the deployment environment. Specific focus is needed on the resource constraints that exist at edge devices.
5. Open application
   1. If you have a proven and strong academic track record and you want to work on your research ideas that are related to computer vision and machine learning for mobile autonomous systems, then send us your research proposal.

What are you going to do:

You are going to carry out AI research in one of the projects mentioned above, as part of the MPS lab. There will be regular interactions with researchers at NXP Semiconductors. At the Eindhoven University of Technology, your primary supervisor will be dr. Gijs Dubbelman. The goal of the research is to develop and validate new machine learning and computer vision methods within the context of one of the three research projects;

• Collaborate with other researchers within the MPS lab.
• Regularly present intermediate research results at international conferences and workshops, and publish them in proceedings and journals.
• Assist in relevant teaching activities.
• And, in case of a PhD position, complete and defend a PhD thesis within the official appointment duration of four years.

Your experience and profile:

• A Master's degree in Artificial Intelligence, Computer Science, Engineering, or related field.
• An excellent education profile (cum laude) and a (about to be) published MSc. thesis.
• A strong background in computer vision and machine learning.
• Excellent programming skills in Python.
• Solid mathematics foundations, especially statistics, calculus and linear algebra.
• You are highly motivated and creative.
• You have a research-oriented attitude with an analytical mindset.
• You like to write publications and give presentations on your research.
• Excellent communication, presentation, and writing skills in English.