About the position

Position as Postdoctoral Research Fellow available at Department for Informatics with the research group Digital Signal Processing and Image Analysis (https://www.mn.uio.no/ifi/english/research/groups/dsb/index.html) as part of Visual Intelligence (http://visual-intelligence.no), Norway’s leading research centre in deep learning for image analysis.

The appointment will be for a fixed-term full-time position with a duration between three (3) and four (4) years. Between 10% and 25% of the appointment will be devoted to career-enhancing compulsory work duties, e.g., teaching or supervision. The fellowship duration will be determined at the time of appointment, de-pendent on the qualifications of the applicant and the teaching-related needs of the department.

No one can be appointed for more than one Postdoctoral Research Fellowship at the University of Oslo.

All candidates and projects will have to undergo a check versus national export, sanctions and security regulations. Candidates may be excluded based on these checks. Primary checkpoints are the Export Control regulation, the Sanctions regulation, and the national security regulation.

Job description / Project description /development plan

You are keen on contributing to new advances in deep learning methodology for cardiac ultrasound imaging. Deep learning methods for cardiac ultrasound can, e.g., be used for automatic measurements from image sequences, to assess im-age quality. Your research can also take benefits in recent research in foundational neural models, where models learn from large unlabelled image datasets, but also on additional data like clinical reports or electronic health records. The work will be done in collaboration with GE Heathcare and their research center in Oslo.

The successful candidate will be responsible for developing advanced deep learning models to analyze cardiac anatomical measurements, with a particular emphasis on Left Ventricular (LV) measurements. They will apply semi-supervised learning techniques to address challenges associated with limited labeled data, and utilize domain adaptation methods to transfer models from synthetic to real-world ultrasound data analysis. The candidate will contribute to the creation of automated systems that improve the efficiency, accuracy, and scalability of cardiac diagnostics, ultimately advancing clinical decision-making processes in cardiology.

The position is in the Digital Signal Processing and Image Analysis (DSB) research group, Section for Machine Learning, Department of Informatics. For more information about the position, see https://www.visual-intelligence.no/about/open-positions You will be part of Visual Intelligence and the DSB research group. We expect that you will engage in collaborative research with other members of the centre and the research group. You will collaborate with user partners within Visual Intelligence, to contribute to the centre’s seminars, to collaborate across innovations areas within the centre, and to seek collaboration between the research partners within the centre. You will be part of a network of young researchers in deep learning in the Visual Intelligence Graduate School https://www.visualintelligence.no/about/vigs

Postdoctoral fellows who are appointed for a period of four years are expected to acquire basic pedagogical competency in the course of their fellowship period within the duty component of 25 %.

The main purpose of a postdoctoral fellowship is to provide the candidates with enhanced skills to pursue a scientific top position within or beyond academia. To promote a strategic career path, all postdoctoral research fellows are required to submit a professional development plan no later than one month after commencement of the postdoctoral period.

Qualification requirements

The Faculty of Mathematics and Natural Sciences has a strategic ambition to be among Europe’s leading communities for research, education and innovation. Candidates for these fellowships will be selected in accordance with this, and are expected to be in the upper segment of their class with respect to academic credentials.

Applicants must hold a degree equivalent to a Norwegian doctoral degree in Machine Learning, Artificial Intelligence, or a closely related discipline.
Extensive experience with deep learning frameworks such as PyTorch or Ten-sorFlow, including the development, training, and fine-tuning of complex models for high-dimensional imaging data.
Doctoral dissertation must be submitted for evaluation by the application closing date for the current position. Only applicants with an approved doctoral thesis and public defence are eligible for appointment.
Fluent oral and written communication skills in English.

Desired qualifications:

Solid experience in terms of publications in machine learning for medical imaging, in particular cardiac ultrasound imaging
Proven expertise in semi-supervised learning techniques
Proficiency in leveraging GPUs and distributed training for large-scale datasets is highly desirable
Good background in image analysis/computer vision.

Personal skills

Solid analytical skills
Dedication to work with important applications in medical ultrasound
Ability to lead and conduct research in a collaborative environment
Ability to work independently as well as in multidisciplinary teams
Ability to give and receive constructive scientific criticism

We offer

Read more about the benefits of working in the public sector at Employer Portal.

Inclusive worklife and diversity at UiO

Inclusion and diversity are a strength. The University of Oslo has a personnel policy objective of achieving a balanced gender composition. Furthermore, we want employees with diverse professional expertise, life experience and perspectives.

If there are qualified applicants with disabilities, employment gaps or immigrant background, we will invite at least one applicant from each of these categories to an interview.

We hope that you will apply for the position.

More information about gender equality initiatives at UiO can be found here.

Application

Your application must include:

Cover letter (statement of motivation, summarizing scientific work and research interest)
CV (summarizing education, positions, pedagogical experience, administrative experience and other qualifying activity)
Copies of educational certificates, academic transcript of records
A complete list of publications and up to 5 academic works that the applicant wishes to be considered by the evaluation committee. It shall be clearly stated which of these works that have undergone peer-review.
Names and contact details of 2-3 references (name, relation to candidate, e-mail, and telephone number)

The application with attachments must be delivered in our electronic recruiting system, please follow the link “apply for this job”. Foreign applicants are advised to attach an explanation of their University's grading system. Please note that all documents should be in English (or a Scandinavian language).

When applying for the position, we ask you to retrieve your education results from Vitnemålsportalen.no. If your education results are not available through Vitnemålsportalen, we ask you to upload copies of your transcripts or grades. Please note that all documentation must be in English or a Scandinavian language.

It is expected that the successful candidate will be able to complete the project in the course of the period of employment.

General information

The best qualified candidates will invited for interviews.

Applicant lists can be published in accordance with Norwegian Freedom of Information Act § 25. When you apply for a position with us, your name will appear on the public applicant list. It is possible to request to be excluded from this list. You must justify why you want an exemption from publication and we will then decide whether we can grant your request. If we can't, you will hear from us.

Please refer to Regulations for the Act on universities and colleges chapter 3 (Norwegian) and Guidelines concerning appointment to post doctoral and research posts at UiO (Norwegian).

The University of Oslo has a transfer agreement with all employees that is intended to secure the rights to all research results etc.

Contact persons

For further information please contact Professor Anne Solberg, phone: +47 228 52435, e-mail: anne@ifi.uio.no

For questions regarding the recruitment system Jobbnorge, please contact HR Adviser Therese Ringvold, e-mail: therese.ringvold@mn.uio.no

Informasjonskapsler på arbeidsplassen.no

Rapporter annonse