PhD fellow in Computer Science

Updated: 19 days ago
Deadline: 27 Oct 2019

PhD fellow in Computer Science
Department of Computer Science
Faculty of Science
University of Copenhagen

Department of Computer Science, Faculty of Science at University of Copenhagen is offering a PhD scholarship in computational anatomy commencing 01.02.2020 or as soon as possible thereafter.

Description of the scientific environment
The research time will be conducted in the Machine Learning Section at the Department of Computer Science, University of Copenhagen. The Machine Learning Section provides a strong, international environment for research within Machine Learning, Medical Image Analysis, Natural Language Processing and Information Retrieval. The section currently holds about 20 PhD students and 12 postdocs. It is housed within the main Science Campus which is located centrally in Copenhagen. For details, see https://di.ku.dk/english/research/machine-learning/  

Project description
Current computational models of anatomy assume that any human can be represented by the same "template". For instance, a brain with a tumor is assumed to be a 1-1 diffeomorphic deformation of a brain with no tumor. This is obviously incorrect: Organs vary both in condition and relative arrangement, and disease manifests itself as the appearance of tumors, bleeds or other ''artifacts''.
As the assumption of structural similarity between brains is violated, statistical models are incapable of correctly describing both healthy anatomy and patients with a disease.
The derived statistics are therefore incorrect, potentially leading to incorrect computer-aided diagnoses. Recently, deep-learning based approaches have been introduced into the field and offer a model family flexible enough to find good initial guesses for diffeomorphic registration, but more importantly come with the ability to alter images in a non-diffeomorphic way.

The goal of this PhD project is to extend the framework of diffeomorphic image registration to topological transitions. To this end, data-driven modeling is used to inform the registration algorithm about the type and extend of topological changes and the project explores how Deep-Learning can be used to detect topological changes and correct them in a meaningful way.


Project Supervisors are Associate Professor Aasa Feragen, aasa@di.ku.dk  and Assistant Professor Oswin Krause, oswin.krause@di.ku.dk , Department of Computer Science.

Job description
The position is available for a 3-year period and your key tasks as a PhD student at SCIENCE are:

  • To manage and carry through your research project
  • Attend PhD courses
  • Write scientific articles and your PhD thesis
  • Teach and disseminate your research
  • To stay at an external research institution for a few months, preferably abroad
  • Work for the department

Formal requirements
Applicants should hold an MSc degree in Computer Science, Statistics, Math or equivalent with good results and good English skills. Experience in differential geometry, machine-learning or medical imaging would be advantageous. As criteria for the assessment of your qualifications emphasis will also be laid on previous publications (if any) and relevant work experience.

Terms of employment
The position is covered by the Memorandum on Job Structure for Academic Staff.
Terms of appointment and payment accord to the agreement between the Ministry of Finance and The Danish Confederation of Professional Associations on Academics in the State.
The starting salary is currently at a minimum DKK 325.625 (approx. €43,400) including annual supplement (+ pension up to DKK 44,980). Negotiation for salary supplement is possible.

Application Procedure
The application, in English, must be submitted electronically by clicking APPLY NOW below.

Please include

  • Cover Letter, stating which PhD project you are applying for and detailing your motivation and background for applying for the specific PhD project.
  • CV
  • Diploma and transcripts of records (BSc and MSc)
  • Acceptance Letter for the relevant MSc Programme at SCIENCE, if any
  • Other information for consideration, e.g. list of publications (if any),
  • Full contact details (Name, address, telephone & email) of 1-3 professional referees

 The University wishes our staff to reflect the diversity of society and thus welcomes applications from all qualified candidates regardless of personal background.

The deadline for applications is 27 October 2019, 23:59 GMT +1.
After the expiry of the deadline for applications, the authorized recruitment manager selects applicants for assessment on the advice of the Interview Committee. Afterwards an assessment committee will be appointed to evaluate the selected applications. The applicants will be notified of the composition of the committee and the final selection of a successful candidate will be made by the Head of Department, based on the recommendations of the assessment committee and the interview committee.
The main criterion for selection will be the research potential of the applicant and the above mentioned skills. The successful candidate will then be requested to formally apply for enrolment as a PhD student at the PhD school of Science. You can read more about the recruitment process at http://employment.ku.dk/faculty/recruitment-process/
Questions
For specific information about the PhD scholarship, please contact the principal supervisor, Professor Aasa Feragen , Department of Computer Science, afhar@dtu.dk
General information about PhD programmes at SCIENCE is available at http://www.science.ku.dk/phd


Part of the International Alliance of Research Universities (IARU), and among Europe’s top-ranking universities, the University of Copenhagen promotes research and teaching of the highest international standard. Rich in tradition and modern in outlook, the University gives students and staff the opportunity to cultivate their talent in an ambitious and informal environment. An effective organisation – with good working conditions and a collaborative work culture – creates the ideal framework for a successful academic career.


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