Deadline: 30 November 2017
PhD Student Position: Computer Vision: At the department of Electrical Engineering research and education are performed in the areas of Communication and Antenna systems, Systems and Control, Computer vision, Signal processing and Biomedical engineering, and Electric Power Engineering. Our knowledge is of use everywhere where there is advanced technology with integrated electronics. We work with challenges for a sustainable future in society of today, for example in the growing demands concerning efficient systems for communications and electrifying.
We offer a dynamic and international work environment with about 200 employees from more than 20 countries, and with extensive national and international research collaborations with academia, industry and society.
The department provides about 100 courses, of which most are included in the Master’s Programs ”Biomedical Engineering”, “Electric Power Engineering”, ”Systems, Control and Mechatronics” and ”Communication Engineering”.
Read more at www.chalmers.se/en/departments/e2
Information about the research group
The Computer Vision Group conducts research in the field of automatic image interpretation. The group targets both medical applications, such as the development of new and more effective methods and systems for analysis, support and diagnostics, as well as general computer vision applications including autonomously guided vehicles, image-based localization, stereo and structure-from-motion. The main research problems include mathematical theory, algorithms and machine learning (deep learning) for inverse problems such as reconstruction, segmentation and registration.
Information about the project
Recent years have seen the emergence of a number of successful visual reconstruction systems. Using nothing but image data it is now possible to automatically create point cloud models of the viewed scene. State-of-the-art methods are capable of creating city scale reconstructions by processing millions of images in roughly a day. The goal of this project is to develop new reconstruction systems, capable of handling moving and deforming objects. The project addresses mathematical modeling, method development and object representation.
Current state-of-the-art reconstruction methods addressing non-rigid reconstruction are in many cases limited by inefficient representations. For example, traditional rank based methods typically assume that observed point motions belong to a linear subspace of low dimension. This is a very general model that completely ignores the fact that points on the same object are likely to exhibit similar motion. Because of the high complexity of this model, successful inference is highly dependent on the amount of available data. In structure from motion one has to expect that a significant portion of the elements of the measurement matrix are unobserved due to tracking failure and occlusion, the so called missing data problem. This poses a serious problem when the model is too general.
In this project we will design compressive methods capable of estimating compact models from a minimum amount of data. Concepts that can be utilized are for example sparsity, rank, recurrence, self-similarity and spatial context. Work in the area of compressed sensing has demonstrated that compact models utilizing compression criteria often allow for easy inference despite incomplete observations. To achieve our goals the project aims to develop new and more accurate mathematical models and inference methods that are useful in the reconstruction process. In particular, flexible alternatives that can be combined to simultaneously incorporate multiple priors are sought for. We will develop formulations that model the problem more accurately than current methods while still allowing efficient inference.
Your major responsibilities are to pursue your own doctoral studies. You are expected to develop your own scientific concepts and communicate the results of your research verbally and in writing, both in Swedish and in English. The position generally also includes teaching on Chalmers’ undergraduate level or performing other duties corresponding to 20 per cent of working hours.
Full-time temporary employment. The position is limited to a maximum of five years.
To qualify as a PhD student, you must have a master’s level degree corresponding to at least 240 higher education credits in physics, mathematics or computer science.
The position requires sound verbal and written communication skills in Swedish and English. If Swedish is not your native language, you should be able to teach in Swedish after two years. Chalmers offers Swedish courses
Chalmers continuously strives to be an attractive employer. Equality and diversity are substantial foundations in all activities at Chalmers.
The application should be marked with Ref 20170472 and written in English. The application should be sent electronically and be attached as pdf-files, as below:
CV: (Please name the document: CV, Family name, Ref. number)
• Other, for example previous employments or leadership qualifications and positions of trust.
• Two references that we can contact.
Personal letter: (Please name the document as: Personal letter, Family name, Ref. number)
• 1-3 pages where you introduce yourself and present your qualifications.
• Previous research fields and main research results.
• Future goals and research focus. Are there any specific projects and research issues you are primarily interested in?
• Copies of bachelor and/or master’s thesis.
• Attested copies and transcripts of completed education, grades and other certificates, eg. TOEFL test results.
Please use the button at the foot of the page to reach the application form. The files may be compressed (zipped).
Application deadline: 30 November 2017
*** Chalmers declines to consider all offers of further announcement publishing or other types of support for the recruiting process in connection with this position. ***
Chalmers University of Technology conducts research and education in engineering sciences, architecture, technology-related mathematical sciences, natural and nautical sciences, working in close collaboration with industry and society. The strategy for scientific excellence focuses on our eight Areas of Advance; Building Futures, Energy, Information & Communication Technology, Life Science, Materials Science, Nanoscience & Nanotechnology, Production and Transport. The aim is to make an active contribution to a sustainable future using the basic sciences as a foundation and innovation and entrepreneurship as the central driving forces. Chalmers has around 11,000 students and 3,000 employees. New knowledge and improved technology have characterised Chalmers since its foundation in 1829, completely in accordance with the will of William Chalmers and his motto: Avancez!