Postdoctoral Fellow: AI and Biology: The University of Oslo (UiO) is seeking a talented and enthusiastic Postdoctoral Research Fellow to join the AUTORHYTHM Convergence Environment at the Department of Biosciences. This full-time position is for three years, focusing on developing new machine learning methods to explore the spatiotemporal nature of autophagy and its impact on healthy lifespan.
Postdoctoral Research Fellow at the Intersection of Artificial Intelligence and Biology
Summary Table:
| Category | Details |
|---|---|
| Designation | Postdoctoral Research Fellow |
| Research Area | Artificial Intelligence and Biology |
| Location | Department of Biosciences, University of Oslo, Norway |
| Eligibility/Qualification | Doctoral degree in biology, biostatistics, or computer science, with documented coding skills and experience in machine-learning-based analysis of biological or medical images. |
| Job Description | Develop deep learning algorithms to analyze the spatiotemporal nature of autophagy, using existing high-content time-series imaging data. |
| How to Apply | Submit a cover letter, CV, educational certificates, list of publications, and references through UiO’s electronic recruiting system. |
| Last Date for Apply | 30th June 2024 |
Designation:
Postdoctoral Research Fellow
Research Area:
Artificial Intelligence and Biology
Location:
Department of Biosciences, University of Oslo, Norway
Eligibility/Qualification:
- Doctoral degree equivalent to a Norwegian doctoral degree in biology, biostatistics, or computer science.
- Documented coding skills and experience with machine-learning-based analysis of biological or medical images.
- Experience with deep learning methods, explainable AI methods, and quantification of uncertainty.
- Knowledge of autophagy regulation is advantageous.
- Fluent oral and written communication skills in English.
- Personal suitability and motivation for the position.
Job Description:
The Postdoctoral Research Fellow will join the AUTORHYTHM Convergence Environment, focusing on:
- Developing machine learning methods for analyzing the spatiotemporal nature of autophagy.
- Utilizing existing high-content time-series imaging data for image segmentation, detection, and classification.
- Predicting autophagy properties and quantifying uncertainty in new data.
- Exploring synthetic data generation and extending performance metrics.
- Enhancing model interpretability and explainability.
The candidate will work within an interdisciplinary team, including collaboration with the Cell Stress and Cancer group at UiO, Simula Research Laboratory, and the Cancer Molecular Medicine group at Oslo University Hospital.
How to Apply:
Submit the following documents through UiO’s electronic recruiting system:
- Cover letter (statement of motivation, summarizing scientific work and research interest).
- CV (summarizing education, positions, and other qualifying activities).
- Copies of educational certificates and academic transcripts.
- Complete list of publications and up to 5 academic works for evaluation.
- Optional: Link to a repository of recent work (e.g., GitHub).
- Names and contact details of 2-3 references.
Last Date for Apply:
30th June 2024
Contact Persons:
- Helene Knævelsrud: helene.knavelsrud@medisin.uio.no, Phone: +47 41427591
- Michael A. Riegler: michael@simula.no
- Jorrit Enserink: jorrit.enserink@ibv.uio.no
- HR Adviser Nina Holtan: nina.holtan@mn.uio.no
About the University of Oslo: The University of Oslo is Norway’s oldest and highest-rated institution of research and education, with a diverse academic community contributing to societal advancements. The Department of Biosciences conducts research in various biological fields and offers education to a large student body.
Apply for this job: Apply for this job
Deadline: 30th June 2024
Employer: University of Oslo
Municipality: Oslo
Scope: Fulltime
Duration: Project
Place of Service: Problemveien 7, 0313 Oslo
The University of Oslo promotes a balanced gender composition, diversity in expertise, and an inclusive work environment. Qualified applicants with disabilities, employment gaps, or immigrant backgrounds are encouraged to apply.
