Lignende stillinger
A Doctoral Research Fellowship in Machine Learning for Critical Healthcare is available at the Faculty of Computer Sciences, Engineering and Economics at Østfold University College (ØUC).
The research will be conducted within the Machine Learning Research Group at the Department of Computer Science and Communication, in collaboration with Østfold Hospital Trust, and will focus on developing novel computational models for trustworthy applications in critical care.
The doctoral position is part of the research initiative “The Digital Society” at ØUC.
Intensive Care Units (ICUs) generate vast amounts of short, irregular biomedical time-series data, including physiological signals, lab results, and interventions. These data hold great potential for supporting high-stakes, complex decision-making. However, traditional machine learning models face limitations in this domain due to several critical challenges. First, ICU data are high-dimensional and multimodal, with patient states evolving dynamically over time. Second, clinical conditions such as infection, sepsis, ventilation, and hemodynamic instability are often interconnected, necessitating a holistic modeling approach. Third, there is a critical need for improved yet explainable predictive accuracy to enable the early detection of life-threatening conditions.
This project aims to develop a multiway and multitask learning framework that serves as a domain-specific foundation model for several downstream clinical tasks relevant to complex ICU decision-making.
As an initial case study, the PhD project will be linked to an ongoing initiative at Østfold Hospital Trust focusing on the early prediction of ventilator-associated pneumonia (VAP), a severe ICU complication associated with increased mortality and healthcare costs. In addition to developing task-specific predictive models, VAP prediction will serve as a downstream use case to evaluate and benchmark the performance of the proposed model.
The project will address key, long-standing challenges in machine learning for short multivariate time-series analysis. By developing a multiway and multitask learning framework with built-in explainability, the project aims to deliver clinically interpretable and trustworthy AI models for ICU decision-making. Ultimately, the project aims to improve patient care while ensuring safety, fairness, and accountability, thereby contributing to the development of a responsible and sustainable digital society.
The Doctoral Research Fellowship is a full time (100%) fixed term position for 3 years funded by Østfold University College.
The candidate will be employed at Østfold University College. The candidate is expected to apply and be accepted in our Doctoral Programme “Digitalisation and Society”. Read more on our website: Digitalisation and Society
Estimated starting date: 1st of January 2026.
The candidate will be a part of a research team, including scientists from computational and medical sciences working on the development of AI-driven solutions to support decision making in healthcare. The research will be conducted in collaboration with Østfold Hospital Trust.
The candidate is expected to:
. Video: https://youtu.be/UDMirdRpu4w
The successful candidate is required to have
Desired qualifications
We would like you to have
Personal qualifications
The successful candidate has
Emphasis will be placed on the following
In the evaluation of the applicants, emphasis will be placed on education, experience and personal and interpersonal qualities. Motivation, ambitions, and potential will also count in the assessment of the candidates.
Furthermore, emphasis will be placed on the following:
Please submit your application electronically via our recruitment system Jobbnorge.no. All attached documentation must be in a Scandinavian language or in English.
Application must include the following:
All attachments should be included electronically within the application deadline. Other documentation may be claimed at a later stage, e.g., proof of claimed English proficiency.
Documents to be sent as hard copies by post
Applicants with degrees from: Cameroon, Canada, Ethiopia, Eritrea, Ghana, Nigeria, the Philippines and the USA must send their transcripts and diploma of master’s and bachelor’s education as hard copies directly from the relevant college/university, in addition to uploading them online. The hard copies must be received by us within four - 4 - weeks after the application deadline expires.
To send hard copies to HiØ, please use the following address:
Høgskolen i Østfold
HR-section
Postboks 700
1757 HALDEN
Please note that incomplete applications will not be considered.
Admission to the doctoral programme Digitalisation and Society is a condition for appointment as a research fellow. The final plan for research training shall be approved and regulated by contract at the latest three months after the appointment is taken up.
The successful candidate will receive assistance in the application process to the PhD programme Digitalisation and Society at Østfold University College when starting in the position.
The appointment is to be made in accordance with the State Employees law, the act relating to Universities and Universities Colleges and the national guidelines for appointment as a PhD student, a postdoctoral fellow or a research assistant.
You are strongly encouraged to contact the main supervisor for a discussion about the position, relevant references and the project. For other information, please contact the Vice-Dean or the advisors:
The university college is committed to fostering a diverse and gender-balanced workforce. We seek employees with diverse skills, academic backgrounds, life experiences, and perspectives. If there are qualified applicants with disabilities, gaps in their CVs, or immigrant backgrounds, we will invite at least one applicant from each of these groups for an interview.
In accordance with the Norwegian Freedom of Information Act § 25, paragraph 2, information about the applicant may be disclosed even if the applicant has requested not to be included on the list of applicants.
If you disclose that you have an immigrant background, disability, or gaps in your CV, this information, in anonymized form, may be used for statistical purposes.