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PhD Research Fellowship in Computational Immunology and Machine Learning Research
Universitetet i Oslo
The University of Oslo is Norway’s oldest and highest ranked educational and research institution, with 28 000 students and 7000 employees. With its broad range of academic disciplines and internationally recognised research communities, UiO is an important contributor to society.
The Institute of Clinical Medicine (Klinmed) is one of three institutes under the Faculty. Klinmed is responsible for the Faculty's educational and research activities at Oslo University Hospital and Akershus University Hospital. With about 800 employees spread over approximately 425 man-labour years, Klinmed is the university's largest institute. Our activities follow the clinical activity at the hospitals and are spread across a number of geographical areas.
A three-year full-time PhD Research Fellowship position code 1017 in Computational Immunology and Machine Learning 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 January 2021 with a flexible start between January and May 2021. 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 funded by a grant (by the Research Council of Norway), which aims to investigate and develop novel AI and machine learning methods for understanding adaptive immune receptor repertoires (AIRR). AIRR are the effector molecules of the adaptive immune system and are highly sought after for therapy and diagnosis. Their complexity is astronomical and thus poorly understood. Therefore, computational methods and machine learning may help uncover their underlying rules for use in biomedical and public health advances.
The candidate will develop and employ a variety of (deep) machine learning techniques, structural biology and probabilistic Bayesian modeling technology 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. Recent publications by Dr. Greiff may be found on google scholar or on www.greifflab.org.
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. Recent publications by Dr. Sandve may be found on google scholar or on www.sandvelab.org.
Dr. Greiff will be the main supervisor and Dr. Sandve will be co-supervisor for the successful candidate. The labs of Dr. Greiff and Dr. Sandve are closely collaborating on my research projects thus ensuring a very productive, communicative and helpful research environment.
The research fellow must take part in the Faculty’s approved PhD program and is expected to complete the project and to follow the PhD program at the Medical faculty in order to obtain a Ph.D. 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.
- Applicants must hold a Master’s degree in computational biology, mathematics, statistics, (bio)informatics, or a related field. Prior knowledge of biology or immunology is an advantage.
- Experience with machine learning and or other mathematical and computational approaches used in immune repertoire analysis is considered an advantage.
- Experience with structural biology 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.
- 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.
- An exciting research environment with opportunities for academic development.
- Salary NOK 479 600 to NOK 523 200 (Ltr. 54-59) per annum depending on qualifications in position as Phd Research Fellow, position code 1017
- Attractive welfare benefits and a generous pension agreement
- Oslo’s family-friendly environment with its rich opportunities for culture and outdoor activities
How to apply
The application must include:
- Application letter describing the applicant’s qualifications and motivation for the position
- CV (summarizing education, positions, and academic work - scientific publications)
- A complete list of publications
- Masters thesis
- Code samples (e.g., link to own github repository)
- Copies of educational certificates and transcript of records
- List of publications and academic work that the applicant wishes to be considered by the evaluation committee
- Letters of recommendation or 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 links “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).
Applicants, who are invited for an interview, are asked to provide educational certificates, diploma or transcript of records.
Please see the guidelines and regulations for appointments to Research Fellowships at the University of Oslo.
The Fellowship requires admission to the PhD programme at the Faculty of Medicine. Appointment to a PhD research Fellowship is conditional upon admission to the Faculty’s research training programme.
No one can be appointed for more than one PhD Research Fellowship period at the University of Oslo.
According to the Norwegian Freedom of 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.
The appointment may be shortened/given a more limited scope within the framework of the applicable guidelines on account of any previous employment in academic positions.
The University of Oslo has an agreement for all employees, aiming to secure rights to research results etc.
- Associate Professor Victor Greiff, e-mail: [email protected]
- Professor Geir K. Sandve, e-mail: [email protected]
- HR-adviser Karoline Berg-Eriksen, (questions regarding the online application form), e-mail: [email protected]