PhD Candidate: Deep Transfer Learning for Medical Image Analysis

PhD Candidate: Deep Transfer Learning for Medical Image Analysis

Published Deadline Location
17 Jun 30 Jul Nijmegen

Job description

Medical imaging data is often heterogeneous: it is generated using different scanners, different scanning protocols or different imaging modalities. Training machine learning models for medical diagnosis on such data from different domains can be difficult, but transfer learning can help: by transforming input from different domains to a common representation, datasets from multiple sources can be used at train and test time within a single classification model. Learning input transformations yielding improved diagnostic performance using multiple related sources of information is a challenging research task.

In this PhD project you will be developing and testing novel machine learning algorithms for transfer learning that can be applied to improve diagnosis of breast cancer from mammography image data from different sources. The large amount of data made available during this project will make it possible to use cutting-edge techniques based on deep learning.

The project involves three partners: the Radiology department of the Radboud university medical center, the Data Science group of the Institute for Computing and Information Sciences (iCIS), and a company called SigmaScreening. You will be working at iCIS and collaborate with other PhD candidates and postdoctoral researchers appointed by iCIS or one of the other project partners.

Specifications

Radboud University

Requirements

You have an MSc degree in computer science, mathematics, or a related discipline. You are open-minded, with a strong interest in multidisciplinary research and a solid background in machine learning, and you are highly motivated to perform scientific research. As you will be working in a multidisciplinary environment, you need to be flexible, communicative and able to work in a multidisciplinary team.

Conditions of employment

  • employment: 1.0 FTE
  • a maximum gross monthly salary of € 2,972 based on a 38-hour working week
  • the starting salary is €2,325 per month on a full-time basis, the salary will increase to €2,972 per month in the fourth year (P scale)
  • in addition to the salary: an 8% holiday allowance and an 8.3% end-of-year bonus
  • duration of the contract: 4 years
  • your performance will be evaluated after 18 months. If the evaluation is positive, the contract will be extended
  • you will be classified as a PhD Candidate (promovendus) in the Dutch university job-ranking system (UFO)
  • you will be able to make use of our Dual Career Service where our Dual Career Officer will assist with family related support, such as child care, and help your partner prepare for the local labour market and with finding an occupation

Are you interested in our excellent employment conditions?

Employer

Strategically located in Europe, Radboud University is one of the leading academic communities in the Netherlands. A place with a personal touch, where top-flight education and research take place on a beautiful green campus in modern buildings with state-of-art facilities.

Department

Faculty of Science
The Data Science group’s research concerns the design and understanding of (probabilistic) machine learning methods, with a keen eye on applications in other scientific domains as well as industry. The Data Science section is part of the vibrant and rapidly expanding Institute for Computing and Information Sciences (iCIS). iCIS is consistently ranked as the top Computer Science department in the Netherlands (National Research Review of Computer Science 2002-2008 and 2009-2014).

Additional information

Prof. Elena Marchiori
elenam@cs.ru.nl

No commercial propositions please.

Specifications

  • PhD positions
  • Natural Sciences
  • max. 38 hours per week
  • max. €2972 per month
  • University Graduate
  • 62.48.19

Employer

Location

Comeniuslaan 4, 6525 HP, Nijmegen

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Application procedure

Application procedure

Make sure to apply no later than 30 Jul 2019 23:59 (Europe/Amsterdam).