Ph.D. Research Fellowship in Computational Immunology Research 2019, University of Oslo, Norway

University of Oslo, Norway


July 12, 2019


Opportunity Cover Image - Ph.D. Research Fellowship in Computational Immunology Research 2019, University of Oslo, Norway

Ph.D. Research Fellowship in Computational Immunology Research

Job description

A three-year full-time PhD Research Fellowship (position code 1017) in Computational Immunology is available at the Department of Immunology, Institute of Clinical Medicine, The Faculty of Medicine, University of Oslo.

More about the position

The position is available from September 2019 with a flexible start between September and December 2019. The position will be located in the laboratories of Dr. Greiff (Lab for Computational and Systems Immunology) and Dr. Sandve (Laboratory for Biomedical Informatics). The announced position is part of the UiO Convergence Environment ImmunoLingo, which is headed by Dr. Greiff. The primary objective of ImmunoLingo is to decipher how disease and antigen recognition is encoded in the immune system and to perform experiments in silico to improve intervention and treatment of human diseases.

The candidate will develop and employ a variety of (deep) machine learning techniques, and probabilistic Bayesian modeling to quantitatively characterize and predict pathogen recognition by the immune system. Computational prediction of immune recognition is a long-standing computational and immunological problem. Improving computational methods for immune recognition is crucial for the development of personalized and precision medicine approaches such as next-generation infection, cancer, and autoimmune immunodiagnostics and immunotherapeutics. The candidate will be expected to closely collaborate with machine learning experts, statisticians, computational and experimental immunologists as well as clinicians.

The Greiff Lab focuses on the quantitative understanding of adaptive immune receptor (antibody and T-cell receptor) specificity using high-throughput experimental and computational immunology combined with machine learning. The long-term aim is to conceive in-silico novel immunodiagnostics and immunotherapeutics using the disease-diagnostic information and therapeutic potential that is directly encoded into adaptive immune receptors. The advent of high-throughput sequencing has enabled an unprecedented accumulation of big immune repertoire sequencing data. However, as of yet, we lack the computational methods that help us decode the immune grammar that translates immune sequencing data to immune state diagnosis and prediction of antigen binding. We believe that learning to read and write the immune repertoire language is key for the development of entirely novel, nature-inspired precision medicine immunodiagnostics and immunotherapeutics. Recent publications by Dr. Greiff may be found on google scholar.

The Sandve Lab aims to delineate and model how the receptor sequence determines which antigens are recognized by a given B or T cell. In particular, to characterize the typical shapes of regions within receptor sequence space associated with recognition of a given epitope. This is approached by characterizing statistical dependencies and compositional features of receptor sequences, and using this to guide the development of machine learning methods for classifying antigen recognition of individual receptors and classifying disease states of repertoires. This methodological inquiry is combined with the development of a robust software platform that allows both novices and experts to perform immune repertoire analyses in an efficient and reproducible manner. Recent publications by Dr. Sandve may be found on google scholar.

Dr. Greiff will be the main supervisor and Dr. Sandve will be co-supervisor for the successful candidate.

The research fellow must take part in the Faculty’s approved PhD program and is expected to complete the project within the set fellowship period. The main purpose of the fellowship is research training leading to the successful completion of a PhD degree.

The applicant must, in collaboration with her/his supervisor, within 3 months after employment, have worked out a complete project description to be attached to the application for admission to the doctoral program. For more information, please see our web site.

Qualification requirements

  • Applicants must hold a Master’s degree in computational biology, bioinformatics, informatics, statistics or a related field.  Prior knowledge of biology or immunology is an advantage.
  • Experience with machine learning and or other mathematical approaches used in immune repertoire analysis is considered an advantage.
  • The candidate should be motivated to both learn advanced machine learning and gain insights on the characteristics of B and T-cell receptors in health and disease (autoimmunity).
  • The candidate will work in a very ambitious interdisciplinary setting which will require high flexibility.
  •  Fluent oral and written communication skills in English.

The Faculty of Medicine has a strategic ambition of being a leading research faculty. Candidates for these fellowships will be selected in accordance with this, and expected to be in the upper segment of their class with respect to academic credentials.

We offer

  • An exciting research environment with opportunities for academic development.
  •  Salary NOK 479 600 to NOK 513 600 per annum depending on qualifications in a position as PhD Research fellow (position code 1017).
  • Attractive welfare benefits and a generous pension agreement, in addition to Oslo’s family-friendly environment with its rich opportunities for culture and outdoor activities

For more information click "LINK TO ORIGINAL" below.

This opportunity has expired. It was originally published here:

Eligible Countries
Host Country
Study Levels
Publish Date
June 17, 2019