10 PhD candidates or Postdoctoral researchers in Machine Learning and Deep Learning
Faculty of Science – Institute of Informatics
The Faculty of Science holds a leading position internationally in its fields of research and participates in a large number of cooperative programs with universities, research institutes and businesses. The faculty has a student body of around 6,000 and 1,800 members of staff, spread over eight research institutes and a number of faculty-wide support services. A considerable part of the research is made possible by external funding from Dutch and international organizations and the private sector. The Faculty of Science offers thirteen Bachelor's degree programs and eighteen Master’s degree programs in the fields of the exact sciences, computer science and information studies, and life and earth sciences.
Since September 2010, the whole faculty has been housed in a brand new building at the Science Park in Amsterdam. The installment of the faculty has made the Science Park one of the largest centers of academic research in the Netherlands.
The Informatics Institute is one of the large research institutes with the faculty, with a focus on complex information systems divided in two broad themes: 'Computational Systems' and 'Intelligent Systems.' The institute has a prominent international standing and is active in a dynamic scientific area, with a strong innovative character and an extensive portfolio of externally funded projects.
World-class research groups directly involved in deep learning are AMLAB (machine learning led by Prof. M. Welling), ISIS (computer vision led by Prof. A. Smeulders) and ILPS (information retrieval led by Prof. M. de Rijke). Besides Bosch-UvA Lab, other examples of industry funded research labs involved in deep learning are Qualcomm-UVA Lab (12 PhDs/Postdoctoral researchers) and SAP-UvALab (3 PhDs/Postdoctoral researchers).
Bosch, a multinational engineering and electronics company, and the University of Amsterdam, a world-leading computer science department, have started a joint research lab in Amsterdam, the Netherlands, to join the best of academic and industrial research. The lab focuses on fundamental research in deep learning with applications to intelligent vehicles. It will host 10 PhD or Postdoc research positions and is led by Prof. Max Welling (machine learning), Prof. Arnold Smeulders (computer vision) and a new tenure track assistant professor, who is currently being recruited. One of the perks of the program is an exchange program where each lab member will stay for one month per year at Bosch Research in Germany.
The lab will pursue world-class research on the following ten topics listed below.
- Project 1: Methods for Semi-supervised Learning and Active Labeling - How can we exploit unlabeled data for a supervised learning problem and how can we identify the most informative subset of examples to be annotated by an expert?
- Project 2: Methods for Robust Feature Learning
How can we learn robust features that remain maximally predictive even if the distribution of test data is very different from the distribution of training data?
- Project 3: Calibrated Uncertainty Estimation
How can we provide reliable confidence intervals for deep neural network predictions?
- Project 4: Methods for Multimodal Learning and Sensor Fusion
How can we combine multiple sources of information to improve prediction accuracy?
- Project 5: Combining Generative Probabilistic Models with Deep Learning
How can we use probabilistic, possibly causal, graphical models, or complex simulators, to improve the accuracy of a classifier?
- Project 6: Model Compression and Distillation
How can we maximally compress the amount of bits necessary to store and execute a deep neural network while maintaining high accuracy?
- Project 7: Reinforcement Learning and Planning
How can we use RL to plan the actions of e.g. a car in traffic, given sensory information of its surroundings?
- Project 8: Learning color-invariant bases
Can robust, universally applicable color-invariants be learned in the lower layers of CNN’s that facilitate image classification?
- Project 9: Learning to follow objects over multiple cameras
Can we learn the characteristics of objects as observed from multiple camera’s images without a priori knowledge on the camera’s properties, their frames or the objects?
- Project 10: Learning from images near the boundary of a class
How can we learn from adversarial examples or hard positive/negative examples and how can we make classifiers perform robustly when confronted with adversarial examples?
Applications may only be submitted by sending your application to email@example.com. Please do not send or cc your application to the directors (Prof. M. Welling and Prof. A. Smeulders). To process your application immediately, please quote vacancy number 16-580 and the position and the project you are applying for in the subject-line.
Applications must include a:
- motivation letter explaining why you are the right candidate;
- curriculum vitae, (max 3 pages);
- copy of your Master’s thesis or PhD thesis (when available);
- complete record of Bachelor and Master courses (including grades);
- list of projects you have worked on (with brief descriptions of your contributions, max 2 pages) and
- the names and contact addresses of at least two academic references.
Also indicate a ranked list of the top-3 of projects you would like to work on and why. All these should be grouped in one PDF attachment.
The committee does not guarantee that late or incomplete applications will be considered.
When you apply, please make sure you apply to the correct address and always indicate to which of the ten projects you are applying.
The selection process commences immediately and continues until a suitable candidate is found. Applications will be accepted until 15 February 2017.
For more information please click "Further Official Information" below.