PhD Position Online 3D Scene Representation Learning

Faculty/Services:  Faculty of Science
Educational level:  Master
Function type:  PhD position
Closing date:  15 July 2022
Vacancy number:  8018

 

 

Are you excited about creating a digital twin of the 3D world around you? The next generation of autonomous cars, robots or mixed reality devices will require an efficient and scalable learned 3D representation of the scene which can jointly serve a multitude of downstream tasks.

 

Desirable goals and possible research directions are:

  • Learned online scene updates from input data streams, collaborative/crowd-based updating (sequence to vector learning) of a scene representation;
  • Rich multi-modal scene representations (geometry, texture, material, semantics, object relations, usage information, etc.);
  • Self-supervised/weakly supervised learning with input data reproduction via differentiable rendering techniques;
  • Efficient data structures for learned scene encodings;
  • Multi-sensor data fusion.

 

Autonomous agents need to process large amounts of data and keep previously seen information in memory in an efficient and compressed manner. For example projects which perform some first steps check out the references down below [1-8].

 

This fully-funded PhD position is within the Computer Vision (CV) lab at the Informatics Institute of the University of Amsterdam (UvA) and ATLAS lab - a collaboration between UvA and TomTom. The goal of ATLAS lab is the development new machine learning-based algorithms for high definition map creation for self-driving vehicles. The ATLAS lab is also part of the Innovation Center for Artificial Intelligence (ICAI) such that there are plenty of possibilities to network and benefit from the participation of related events. ATLAS lab is located in Science Park, Amsterdam.

 

Live in one of the most vibrant and attractive cities of Europe, benefit from the dense network of strong research groups in computer vision and machine learning both in academia as well as leading industry labs and startups!

 

What are you going to do?

 

Tasks and responsibilities:

  • Design and implement novel algorithms/data structures for learned 3D scene representations.
  • You will work at the intersection of 3D computer vision, (inverse) computer graphics, machine learning, and optimization to develop next generation mapping and scene understanding algorithms
  • Collaborate with other researchers within the lab and TomTom.
  • Attend and network at international conferences and summer schools
  • Pursue and complete a PhD thesis within the appointed duration of four years.
  • Publish research results at top-tier international conferences and journals.
  • Assist in teaching activities such as lab assistance and student supervision

 

What do you have to offer?

 

Your experience and profile

  • A Master’s degree in Artificial Intelligence, Computer Science, Mathematics, or a closely related field.
  • A strong scientific and mathematical background in deep learning and computer vision.
  • A deep interest, passion, self-motivation and excitement in solving new problems and developing new algorithms for large-scale scene understanding.
  • Good programming skills, experience with C++/Python and deep learning frameworks (PyTorch/Tesnorflow/JAX).
  • A good academic record and eagerness to tackle core scientific problems.
  • Fluent in English, both written and spoken (Dutch language skills are not required).

 

Our offer

 

A temporary contract for 38 hours per week for the duration of four years (the initial contract will be for a period of 18 months and after satisfactory evaluation it will be extended for a total duration of 4 years). The preferred starting date is as soon as possible. This should lead to a dissertation (PhD thesis). We will draft an educational plan that includes attendance of courses and (international) meetings. We also expect you to assist in teaching undergraduates and Master students.

 

The gross monthly salary, based on 38 hours per week and dependent on relevant experience, ranges between € 2,443 to € 3,122 (scale P). This does not include the 8% holiday allowance and the 8,3% year-end allowance the UvA offers. A favourable tax agreement, the ‘30% ruling’, may apply to non-Dutch applicants. The Collective Labour Agreement of Dutch Universities (CAO NU) is applicable.

 

Besides the salary and a vibrant and challenging environment at Science Park we offer you multiple fringe benefits:

  • 232 holiday hours per year (based on fulltime) and extra holidays between Christmas and 1 January;
  • Multiple courses to follow from our Teaching and Learning Centre;
  • A complete educational program for PhD students;
  • Multiple courses on topics such as leadership for academic staff;
  • Multiple courses on topics such as time management, handling stress and an online learning platform with 100+ different courses;
  • 7 weeks birth leave (partner leave) with 100% salary;
  • Partly paid parental leave;
  • The possibility to set up a workplace at home;
  • A pension at ABP for which UvA pays two third part of the contribution;
  • The possibility to follow courses to learn Dutch;
  • Help with housing for a studio or small apartment when you’re moving from abroad.

 

Are you curious about our extensive package of secondary employment benefits like our excellent opportunities for study and development? Take a look here.

 

About us

 

The University of Amsterdam is the Netherlands' largest university, offering the widest range of academic programmes. At the UvA, 30,000 students, 6,000 staff members and 3,000 PhD candidates study and work in a diverse range of fields, connected by a culture of curiosity.

 

The Faculty of Science has a student body of around 8,000, as well as 1,800 members of staff working in education, research or support services. Researchers and students at the Faculty of Science are fascinated by every aspect of how the world works, be it elementary particles, the birth of the universe or the functioning of the brain. 

 

The mission of the Informatics Institute (IvI) is to perform curiosity-driven and use-inspired fundamental research in Computer Science. The main research themes are Artificial Intelligence, Computational Science and Systems and Network Engineering. Our research involves complex information systems at large, with a focus on collaborative, data driven, computational and intelligent systems, all with a strong interactive component.

 

The Computer Vision (CV) research group focuses on studying core computer vision technologies and in particular colour processing, 3D reconstruction, object recognition, and human-behaviour analysis. The aim is to provide theories, representation models and computational methods which are essential for image and video understanding. Research ranges from image processing (filtering, feature extraction, reflection modeling, and photometry), invariants (color, descriptors, scene), image understanding (physics‐based, probabilistic), object recognition (classification and detection) to activity recognition with a focus on human‐behavior (eye tracking, facial expression, head pose, age and gender).

 

Atlas Lab is a collaboration between ‘location technology specialist’ TomTom (TOM2) and the University of Amsterdam (UvA). The public-private research lab focuses on using Artificial Intelligence (AI) for developing advanced, highly accurate and safe high definition (HD) maps for self-driving vehicles. The lab is based at Amsterdam Science Park.

 

Any Questions?

 

Do you have questions about this vacancy? Or do you want to know more about our organization? Please contact:

 

Job application

 

The Informatics Institute strives for a better gender balance in its staff. We therefore strongly encourage women to apply for this position.

 

If you feel the profile fits you, and you are interested in the job, we look forward to receiving your application. You can apply online via the button below. We accept applications until and including 15 July 2022.

 

Applications should include the following information (all files besides your CV should be submitted in one single pdf file): 
 

  • A CV, including a list of publications if applicable;
  • A letter of motivation;
  • A link to your Master’s thesis;
  • A complete record of your Bachelor and Master courses, including grades and explanation of grading system;
  • The names and mail addresses of two academic references (no reference letters required, but can be added if available).

 

Please mention the months (not just years) in your CV when referring to your education and work experience. You can use the CV field to upload your resume as a separate pdf document.

Use the Cover Letter field to upload the other requested documents, including the motivation letter, as one single pdf file.

 

Only complete applications received within the response period via the link below will be considered.

 

We will invite potential candidates for interviews within two weeks after the closing date.

 

We are looking forward to your application!

 

References:

[1]    NICE-SLAM: Neural Implicit Scalable Encoding for SLAM,
Zihan Zhu, Songyou Peng, Viktor Larsson, Weiwei Xu, Hujun Bao, Zhaopeng Cui, Martin R. Oswald, Marc Pollefeys. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. https://pengsongyou.github.io/nice-slam  
[2]    NeuralFusion: Online Depth Fusion in Latent Space,
Silvan Weder, Johannes L. Schönberger, Marc Pollefeys, and Martin R. Oswald.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
https://www.silvanweder.com/publications/neural-fusion/
[3]    DeepSurfels: Learning Online Appearance Fusion,
Marko Mihajlovic, Silvan Weder, Marc Pollefeys, and Martin R. Oswald.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
https://onlinereconstruction.github.io/DeepSurfels
[4]    NVS-MonoDepth: Improving Monocular Depth Prediction with Novel View Synthesis,
Zuria Bauer, Zuoyue Li, Sergio Orts-Escolano, Miguel Cazorla, Marc Pollefeys, and Martin R. Oswald. International Conference on 3D Vision (3DV), 2021.
https://arxiv.org/abs/2112.12577
[5]    Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects,
Denys Rozumnyi, Martin R. Oswald, Vittorio Ferrari, and Marc Pollefeys. NeurIPS, 2021. https://github.com/rozumden/ShapeFromBlur 
[6]    Motion-from-Blur: 3D Shape and Motion Estimation of Motion-blurred Objects in Videos, Denys Rozumnyi, Martin R. Oswald, Vittorio Ferrari, and Marc Pollefeys. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. https://github.com/rozumden/ShapeFromBlur
[7]    RoutedFusion: Learned Real-time Depth Map Fusion,
Silvan Weder, Johannes L. Schönberger, Marc Pollefeys, and Martin R. Oswald.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
https://github.com/weders/RoutedFusion
[8]    KAPLAN: A 3D Point Descriptor for Shape Completion,
Audrey Richard, Ian Cherabier, Ma
rtin R. Oswald, Marc Pollefeys, and Konrad Schindler. International Conference on 3D Vision (3DV), 2020.
https://arxiv.org/abs/2008.00096

 

The UvA is an equal-opportunity employer. We prioritize diversity and are committed to creating an inclusive environment for everyone. We value a spirit of enquiry and perseverance, provide the space to keep asking questions, and promote a culture of curiosity and creativity.

 

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