Phd/doctoral Tromsø, Norway Apply
Job
UiT The Arctic University of Norway
Dept. of Physics and Technology/Machine Learning Group
Department of Physics and Technology, Faculty of Science and Technology, UiT The Arctic University of Norway,
9037 Tromsø
Norway
The address could not be found.

Application through Jobbnorge: https://www.jobbnorge.no/en/available-jobs/job/262829/phd-fellow-in-deep-learning-for-spatio-temporal-medical-image-analysis

Application Deadline: June 4, 2024

 

The position

Visual Intelligence at the Department of Physics and Technology announces a vacant PhD position in the area of deep learning and medical image analysis.

The position is for a period of three years. The objective of the position is to complete research training to the level of a doctoral degree.

Admission to a PhD program is a prerequisite for employment. The workplace is at UiT in Tromsø. You must be able to start in the position in Tromsø within a reasonable time and no later than 6 months after receiving the offer.

The studentship affiliation

The successful candidate will work at the research centre Visual Intelligence at the Department of Physics and Technology. The centre is a Norwegian centre for research-driven innovation, funded by the Research Council of Norway and consortium partners.

The goal of Visual Intelligence is to develop novel deep learning-based solutions to extract knowledge from complex image data to enable new innovations. Deep learning has led to a range of new image-based technologies that are rapidly changing society. Despite these advances, it is still a long way before the potential of deep learning for visual intelligence is realized for applications and industries relying on more complex visual data, e.g., within medicine and health, the marine sciences, the energy sector, and within earth observation.

Visual Intelligence conducts research within machine learning, more specifically within deep learning (deep artificial neural networks). The research focus is broadly on developing the next generation of deep learning solutions in order to:

  • Learn from limited data
  • Capture context and dependencies (e.g., prior knowledge)
  • Enable reliable systems capable of quantifying uncertainty associated with their own predictions and operations, and
  • Develop interpretable learning methodology.

Visual Intelligence aims to enable innovations across four interrelated Innovation Areas (medicine and health, marine sciences, energy, earth observation), all relying on creating value from complex image data. The PhD position is affiliated with Innovation Medicine and Health. The work will be done in collaboration with user partners of Visual Intelligence, such as the University Hospital of North Norway (UNN) or General Electric Healthcare

Field of research and the role of the PhD Fellow

The focus of this PhD fellowship is on developing deep learning methods for spatio-temporal medical image analysis, such as dynamic positron emission tomography or echocardiography. The PhD student will address challenges in uncertainty prediction, explainability, self-supervised learning, multimodal and temporal information fusion, and the integration of medical domain knowledge to enhance network training and task performance.

The core of the position involves creating new deep learning techniques for spatio-temporal data analysis with limited labels. Key research aspects include estimating and modeling uncertainty, particularly developing new methods for spatio-temporal imaging data. The aim is to establish trustworthy and reliable deep learning solutions for clinical use. Developing explainable methods for spatio-temporal data is a top priority, including method evaluation using medical domain knowledge from experts or additional imaging modalities that provide complementary anatomical information.

You are expected to collaborate with other members of the Visual Intelligence center and the Machine Learning research group, across various partners and innovation areas. You will contribute to the centre’s seminars and be part of a network of young researchers in deep learning in the Visual Intelligence Graduate School

 

Contact

For further information about the position, please contact Associate Professor Elisabeth Wetzer:

or Associate Professor Kristoffer Wickstrøm: 

  • phone:+47 776 23216

email: kristoffer.k.wickstrom@uit.no


 

Qualifications

We are seeking a motivated candidate with expertise in deep learning and a strong background in mathematics. Preference will be given to those with experience in related topics.

Candidates must have a Norwegian master's degree or equivalent in physics, mathematics/statistics, computer science, or a related field. Applicants nearing master’s degree completion may also apply. Candidates must demonstrate significant coursework in machine learning, pattern recognition, statistics, deep learning, and programming. Additional coursework in signal processing and physics is advantageous. Key qualifications include:

  • Strong mathematical background
  • Experience in machine learning and related fields
  • Programming proficiency

If your MSc thesis had a strong element of mathematical modelling for development of neural networks, this will be considered a big advantage. Experience with analysis of spatio-temporal medical image data and interdisciplinary collaboration with clinicians will be considered a plus. Since the project will revolve around neural network research, experiences with software tools such as PyTorch/Tensorflow are qualifications we are looking for. Qualifications in terms of relevant publications for this position will be weighted positively. Other essential skills include:

  • Independence and self-motivation
  • Creativity and innovative thinking
  • Strong work ethic and job commitment

Applicants must be fluent in English and capable of working in an international setting. Nordic applicants can prove English proficiency with a high school diploma. Knowledge of Norwegian or a Scandinavian language is a plus.

The selection will focus on the applicant's potential for research education, based on their master's thesis or similar scientific work, and other relevant experiences. Motivation and personal suitability for the position will also be considered.

The opportunity for organized research training is open to as many as possible. Those who already hold a PhD or equivalent will not be considered for this position.

Admission to the PhD programme 

For employment in the PhD position, you must be qualified for admission to the PhD programme at the Faculty of Science and Technology and participate in organized doctoral studies within the employment period.

Admission normally requires:  

  • A bachelor's degree of 180 ECTS and a master's degree of 120 ECTS, or an integrated master's degree. 

UiT normally accepts higher education from countries that are part of the Lisbon Recognition Convention.

In order to gain admission to the programme, the applicant must have a grade point average of C or better for the master’s degree and for relevant subjects of the bachelor’s degree. A more detailed description of admission requirements can be found here

Applicants with a foreign education will be subjected to an evaluation of whether the educational background is equal to Norwegian higher education, following national guidelines from NOKUT. Depending on which country the education is from, one or two additional years of university education may be required to fulfil admission requirements, e.g. a 4-year bachelor's degree and a 2-year master's degree. 

If you are employed in the position, you will be provisionally admitted to the PhD programme. Application for final admission must be submitted no later than two months after taking up the position. 

Inclusion and diversity

UiT The Arctic University of Norway is working actively to promote equality, gender balance and diversity among employees and students, and to create an inclusive and safe working environment. We believe that inclusion and diversity are a strength, and we want employees with different competencies, professional experience, life experience and perspectives.

If you have a disability, a gap in your CV or immigrant background, we encourage you to tick the box for this in your application. If there are qualified applicants, we invite at least one in each group for an interview. If you get the job, we will adapt the working conditions if you need it. Apart from selecting the right candidates, we will only use the information for anonymous statistics.

 

We offer

We offer an interesting project within a highly innovative centre environment, opportunities to travel and meet other leading scientists within the field, independence in your work, a fantastic work environment with nice colleagues, good remuneration. You will work from Tromsø, a lively town with approximately 78.000 inhabitants. It is known for its beautiful scenery, northern lights, midnight sun, as well as the northernmost university in the world and well connected to the rest of Europe. Located on an island surrounded by fjords and mountains, Tromsø is a major cultural hub within the Arctic Circle and a great spot for outdoor activities (hiking, skiing, etc.)

Norwegian health policy aims to ensure that everyone, irrespective of their personal finances and where they live, has access to good health and care services of equal standard. As an employee you will become member of the National Insurance Scheme which also include health care services.

More practical information about working and living in Norway can be found here: https://uit.no/staffmobility


 

Application 

Your application must include: 

  • Cover letter explaining your motivation and highlighting your background and its relevance to the announced job
  • CV (containing a complete overview of education, supervised professional training and professional work).
  • Diploma for bachelor's and master's degree
  • Transcript of grades/academic record for bachelor's and master's degree
  • Explanation of the grading system for foreign education (Diploma Supplement if available)
  • Documentation of English proficiency
  • 3 references with contact information, preferably including the master thesis supervisor
  • Master’s thesis (or draft of thesis if it is not completed), and any other academic works

Qualification with a master’s degree is required before commencement in the position. If you are near completion of your master’s degree, you may still apply and submit a draft version of the thesis and a statement from your supervisor or institution indicating when the degree will be obtained. You must still submit your transcript of grades for the master’s degree with your application.

All documentation to be considered must be in a Scandinavian language or English. Diplomas and transcripts must also be submitted in the original language, if not in English or Scandinavian. If English proficiency is not documented in the application, it must be documented before starting in the position. We only accept applications and documentation sent via Jobbnorge within the application deadline. 
 

General information 

The appointment is made in accordance with State regulations and guidelines at UiT. At our website, you will find more information for applicants

Remuneration for the position of PhD Fellow is in accordance with the State salary scale code 1017. A compulsory contribution of 2 % to the Norwegian Public Service Pension Fund will be deducted. You will become a member of the Norwegian Public Service Pension Fund, which gives you many benefits in addition to a lifelong pension: You may be entitled to financial support if you become ill or disabled, your family may be entitled to financial support when you die, you become insured against occupational injury or occupational disease, and you can get good terms on a mortgage. Read more about your employee benefits at: spk.no.

A shorter period of appointment may be decided when the PhD Fellow has already completed parts of their research training programme or when the appointment is based on a previous qualifying position PhD Fellow, research assistant, or the like in such a way that the total time used for research training amounts to three years. 

We process personal data given in an application or CV in accordance with the Personal Data Act (Offentleglova). According to the Personal Data Act information about the applicant may be included in the public applicant list, also in cases where the applicant has requested non-disclosure. You will receive advance notification in the event of such publication, if you have requested non-disclosure. 
 

Eallju - Developing the High North

UiT The Arctic University of Norway is a multi-campus comprehensive university at the international forefront. Our vision is to be a driving force for developing the High North. The Northern Sami notion eallju, which means eagerness to work, sets the tone for this motive power at UiT. Along with students, staff and the wider community, we aim to utilise our location in Northern Norway and Sápmi, our broad and diverse research and study portfolio and interdisciplinary advantage to shape the future.

Our social mission is to provide research-based education of high quality, perform artistic development and carry out research of the highest international quality standards in the entire range from basic to applied. We will convey knowledge about disciplines and contribute to innovation. Our social mission unites UiT across various studies, research fields and large geographical distances. This demands good cooperation with trade and industry and civil society as well as with international partners. We will strengthen knowledge-based and sustainable development at a regional, national and international level.

Academic freedom and scientific and ethical principles form the basis for all UiT’s activities. Participation, co-determination, transparency and good processes will provide the decision-making basis we need to make wise and far-sighted priorities. Our students and staff will have the opportunity to develop their abilities and potential. Founded on academic integrity, we will be courageous, committed and generous in close contact with disciplines, people and contemporary developments.

We will demonstrate adaptability and seek good and purposeful utilisation of resources, so we are ready to meet the expectations and opportunities of the future. We will strengthen the quality and impact of our disciplines and core tasks through the following three strategic priority areas.

 

 


Application Instructions

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Contact Person
Prof. Elisabeth Wetzer