LEDIG STILLING VED SIMULA

Postdoctoral fellow within explainable artificial intelligence for clinical prognostic tools

Deadline: 31.07.2022

SimulaMet

SimulaMet is a research unit that is jointly owned by Simula Research Laboratory and Oslo Metropolitan University. It is the home of Simula’s research activities on networks and communications, machine learning, and IT management, and it is OsloMet’s strategic partner in research, PhD- and MSc- education in digital engineering. SimulaMet is organized as a limited company and it is part of the Simula Research Laboratory.


The mission of SimulaMet is to do research in digital engineering at the highest international level, to educate and supervise PhD- and master students at OsloMet, and to contribute to innovation in society through collaboration, startup-companies, and licensing of research results.


Learn more about working at Simula: Careers at Simula

Postdoctoral Research Fellow within explainable artificial intelligence for clinical prognostic toolsWe have an open position as Postdoctoral Research Fellow at the Department of Holistic Systems at Simula Metropolitan Center for Digital Engineering (SimulaMet), as part of the EU funded project VALIDATE: Validation of a Trustworthy AI-based Clinical Decision Support System for Improving Patient Outcome in Acute Stroke Treatment.

The candidate will research and implement explanation methods on machine learning systems developed using federated learning, to be used for clinical prognosis.

The candidate will be embedded in the Department of Holistic Systems Research, and in close collaboration with Dr. Inga Strümke and Dr. Michael A. Riegler at SimulaMet.

The appointment is a full-time position and is made for a period of two years.

Project and Job description

The ERC funded project VALIDATE aims at developing, testing and validating AI-based prognostic tools when stratifying patients for stroke treatment.

AI-based prognostic tools and subsequent clinical decision support will only be approved and accepted in clinical settings if they fulfil all necessary criteria with regards to lawfulness, ethics, trustworthiness and robustness. Working with models used for prognosis of stroke outcome, the candidate will contribute to the trustworthiness and robustness as part of the VALIDATE project through

  • Analysis of the models through application and development of explainable AI (XAI) methods. The models are developed using federated learning.
  • Refinement and improvement of the models.
  • Monitoring data distribution and model performance shift during clinical validation.
  • Systematic literature review and workshops on trustworthy AI development.
  • Development of a Trustworthy AI framework.
  • Development of a standard operating procedure (SOP) guideline on trustworthy AI development of an AI-based prognostic tool.

To contribute to integration of AI in healthcare, the project results will be made public in the form of standard operating procedures (SOPs).

The VALIDATE project follows the EU-outlined Trustworthy AI guidelines.

Keywords

Artificial Intelligence

Explainable AI (XAI)

Federated learning

Machine learning

Medicine

Prognostic tools

We are looking for you who

Have a completed (before the position starts) PhD degree in Artificial Intelligence, Machine Learning or Computer Science with excellent grades, and experience developing and implementing XAI methods for machine learning models. Excellent level of spoken and written English is a requirement, as the candidate will work in an international research consortium.

You should ideally have experience working with, meaning using and evaluating, federated learning systems. Experience with clinical decision support systems is an advantage.

SimulaMet is an equal opportunity employer, and women are particularly encouraged to apply.

What we offer

  • Excellent opportunities for performing high quality research, as part of a highly competent and motivated team of international researchers and engineers.
  • An informal and inclusive international working environment.
  • Generous support for travel and opportunities to build international networks, through established collaboration with industry, exchange programs and research visits with other universities, and funding to attend
  • High-standard facilities and offices located in Oslo
  • Numerous benefits: access to company cabin, BabyBonus arrangements, sponsored social events, generous equipment budgets (e.g., computer, phone and subscription), subsidized canteen meals and monthly bus pass, comprehensive travel/health insurance policy, etc.
  • Relocation assistance: accommodation, visas, complimentary Norwegian language courses, etc.
  • Administrative research support: e.g., quality assurance process for grant proposals (including RCN and EU programs).
  • Wellness and work-life balance. Our employees’ health and well-being is a priority and we encourage them to make use of our flexible work arrangements to help balance their work and home lives efficiently.

Application requirements

  • CV summarizing your educational background, working experience (in particular, any relevant academic or industrial work), list of scientific publications, etc.
  • Cover letter: outline your motivation for applying, relevant experience and qualifications, research interests and how/why you are qualified for the position.
  • Academic transcripts, including a copy PhD thesis
  • Contact information of two references, including the PhD supervisor(s).
  • A link to a repository of open source projects if available.

Application deadline: July 31, 2022.

Preferred start is in September 2022.

Applications will be screened continuously, and a decision will be made as soon as we find the right candidate.

Further information

For detailed information about SimulaMet please consult the website.

For more information about our work culture at Simula, see this.

Additional enquiries regarding the position can be addressed to [email protected] or [email protected]

SimulaMet uses Semac’s background check in our recruitment process.

According to the Norwegian Freedom and Information Act (Offentleglova) information about the applicant may be included in the public applicant list, also in cases where the applicant has requested non-disclosure.

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