Ledig stilling ved NMBU

PhD scholarship within medical physics and data science

Deadline: 15.06.2020

Norwegian University of Life Sciences

NMBU has a special responsibility for research and education that ensures the basis of life for future generations.


Sustainability is rooted in everything we do and we provide knowledge for life.


NMBU has 1700 employees and 5200 students and is organized in seven faculties. NMBU has a campus in Ås and in Oslo. In 2021 we are co-located on Ås. Further information on NMBU is available at www.nmbu.no.

About The Faculty of Science and Technology

The Faculty of Science and Technology (REALTEK) develops research-based knowledge and educates civil engineers and lecturers needed to reach the UN's sustainability goals. We have approximately 150 employees, 70 PhD students and soon 1500 students. The education and research at REALTEK cover a broad spectrum of disciplines.


This includes data science, mechanics and process engineering, robotics, construction and architecture, industrial economics, environmental physics and renewable energy, geomatics, water and environmental engineering, applied mathematics as well as secondary school teacher education in natural sciences and use of natural resources such as in agriculture, forestry and aquaculture. The workplace is in Ås, 30 km from Oslo.

Interested in artificial intelligence in medicine? We are looking for a motivated PhD candidate with experience in deep learning and machine learning in medical applications.

About the position

The Faculty of Science and Technology at the Norwegian University of Life Sciences (NMBU) has a vacant three-year PhD–position related to medical physics and data science.

Increasing amounts of health and healthcare data from treatment of animals and humans call for exploration using modern data analysis tools to provide new insight and improve treatment of patients. The project will focus on artificial intelligence in digital veterinary medicine.

The main aim is to develop a software tool that can be used in the clinic for the detection and diagnostics of malignant tissue using medical images.

Deep learning and machine learning approaches to automatic tumour segmentation and classification of tissue as malignant or benign will be central in the project as will visualization of images and results.

Main tasks

The main tasks include:

  • Visualization of images appropriate from a veterinary perspective.
  • Development of a deep learning approach for automatic segmentation of malignant tissue from CT images.
  • Development of an approach for classification of tissue as benign or malignant based on medical images.
  • Development of a software tool appropriate for the veterinary clinic.

The successful candidate is expected to enter a plan for the progress of the work towards a PhD degree during the first months of the appointment, with a view to completing a doctorate within the PhD scholarship period.

Qualification requirements, desired experiences, knowledge and personal qualities

The successful applicant must meet the conditions defined for admission to a PhD programme at NMBU. The applicant must have an academically relevant education corresponding to a five-year Norwegian degree programme, where 120 credits are at master's degree level (candidates submitting an MSc thesis by 15.08.2020 may also be considered). The applicant must have a documented strong academic background from previous studies and document proficiency in both written and oral English. For more detailed information on the admission criteria please see the PhD Regulations and the relevant PhD programme description. The applicant must document expertise and interest in the research subject.

Required Academic qualifications

  • MSc degree in one of the following disciplines:
    • Physics
    • Data science
    • Computer science

Desired qualifications:

  • Medical or biological physics
  • Experience with medical imaging analysis and medical applications
  • Programming skills
  • Machine learning and deep learning skills
  • Image analysis skills
  • Visualization skills

Required personal skills

  • High degree of motivation
  • Curiosity
  • Ability to work and co-operate in an interdisciplinary group
  • Ability to work independently
  • Good communication skills
  • Fluent in English (written and oral)

Remuneration and further information

The position is placed in government pay scale position code 1017 PhD. Fellow. PhD. Fellows are normally placed in pay grade 54 (NOK 479.600) on the Norwegian Government salary scale upon employment and follow ordinary meriting regulations.

Terms of employment are governed by Norwegian guidelines for PhD fellowships at Universities and University Colleges.

For further information, please contact:

Information for PhD applicants and general Information to applicants

Application

To apply online for this vacancy, please click on the 'Apply for this job' button above. This will route you to the University's Web Recruitment System, where you will need to register an account (if you have not already) and log in before completing the online application form.

Application deadline: 15.06.2020

Applications should include (electronically) a letter of intent, curriculum vitae, full publication list, copies of degree certificates and transcripts of academic records (all certified), and a list of two persons who may act as references (with phone numbers and e-mail addresses). Publications should be included electronically within the application deadline. The relevant NMBU Department may require further documentation, e.g. proof of English proficiency.

Printed material which cannot be sent electronically should be sent by surface mail to the Norwegian University of Life Sciences, Faculty of Science and Technology, P.O. Box 5003, NO-1432 Ås, within 15.06.2020. Please quote reference number 20/01576.

If it is difficult to judge the applicant’s contribution for publications with multiple authors, a short description of the applicant’s contribution must be included.

Apply for this job

Powered by Labrador CMS