Ledig stilling på Universitetet i Oslo

Blindern og Urbygningen (Foto: Wikimedia og Colourbox)
Blindern og Urbygningen (Foto: Wikimedia og Colourbox)

Postdoctoral Research Fellow in Machine Learning

Deadline: 10.10.2020

Universitetet i Oslo

The University of Oslo is Norway’s oldest and highest rated institution of research and education with 28 000 students and 7000 employees. Its broad range of academic disciplines and internationally esteemed research communities make UiO an important contributor to society.

The Department of Informatics (IFI) is one of nine departments belonging to the Faculty of Mathematics and Natural Sciences. IFI is Norway’s largest university department for general education and research in Computer Science and related topics.

The Department has more than 1800 students on bachelor level, 600 master students, and over 240 PhDs and postdocs. The overall staff of the Department is close to 370 employees, about 280 of these in full time positions. The full time tenured academic staff is 75, mostly Full/Associate Professors..

About the position

Position as Postdoctoral Research Fellow available at Section of Machine Learning, Department of Informatics

No one can be appointed for more than one Postdoctoral Research Fellowship at the University of Oslo.

The appointment is a fulltime position and is made for a period of three years. Starting date is between Jan 1st and March 1st.

Job description

The adaptive immune system is nature’s most finely-tuned defence tool in that it recognize and neutralise with exquisite specificity any harmful particle (antigen), such as cancer, virus and bacteria. The immune information on past and ongoing immune responses is recorded in genetic sequence information of a myriad immune cells, known as immunogenomic memory. This information may serve as a biomarker across a broad spectrum of immune states (e.g., health, disease, infection, vaccination) and is thus key to the development of next-generation diagnostics and therapeutics.

Machine learning has entered center stage in the biological sciences thanks to its ability to detect, recover and re-create complex signals (e.g., sequence motifs) in large-scale biological data in which noise abounds. The detection of disease signals in immune repertoires belongs to a particularly challenging class of machine learning problems called Multiple instance learning. This is a form of weakly supervised learning where labels are provided only at the level of bags of assorted training instances. Immune repertoire classification is an ideal example of Multiple instance learning, where a given disease state is driven by a small unknown subset of immune cells of a patient. The problem is furthermore a multi-label multiple instance problem, as the immune repertoire of a person will contain a myriad of immune cell subsets corresponding to a lifetime of vaccines, pathogen encounters and more.

The candidate will be developing novel machine learning methodology for the prediction of disease state from immune repertoire sequence data. Both classic regularized regression approaches and modern neural network architectures are of interest for the problem. We are particularly interested in exploring how various heterogeneous sources of additional information, related e.g. to the probabilistic generation process and to the specific recognition capabilities of individual immune receptors, might be exploited through regularization, parameter priors or initialization values for given machine learning models.

Furthermore, we are interested in how causal considerations may help make the models more robust in terms of generalization to relevant clinical settings. Of equal importance to the improvement of machine learning performance for the immune repertoire prediction problems is the use of insights from this particular problem domain to inspire new insights in terms of generic methodology, for instance by making contributions to the multiple instance learning field in general.

The candidate will join a team of PhD students and Postdocs working on machine learning and computational immunology in a tight collaboration between the groups of Geir Kjetil Sandve at Department of Informatics (web page: sandvelab.org) and Victor Greiff at Department of Immunology (lab page: greifflab.org).

The main purpose of a postdoctoral fellowship is to provide the candidates with enhanced skills to pursue a scientific top position within or beyond academia. To promote a strategic career path, all postdoctoral research fellows are required to submit a professional development plan no later than one month after commencement of the postdoctoral period.

Qualification requirements

The Faculty of Mathematics and Natural Sciences has a strategic ambition is to be among Europe’s leading communities for research, education and innovation. 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.

  • Applicants must hold a degree equivalent to a Norwegian doctoral degree in Computer Science, Statistics or a related field. Doctoral dissertation must be submitted for evaluation by the closing date. Only applicants with an approved doctoral thesis and public defence are eligible for appointment
  • Fluent oral and written communication skills in English

The following qualifications will count in the assessment of the applicants:

  • Documented experience with machine learning or statistical methodology development
  • Any experience with immunology would be welcome, but is not required

Personal skills

  • Ability to lead and conduct research in a collaborative environment
  • Ability to give and receive constructive scientific criticism
  • Resourceful, structured, and result-oriented.

We offer

  • Salary NOK 523 200 – 605 500 per year depending on qualifications in position as Postdoctoral Research Fellowship (position code 1352)
  • Attractive welfare benefits and a generous pension agreement
  • Professionally stimulating working environment
  • Vibrant international academic environment
  • Postdoctoral development programmes
  • Oslo’s family-friendly surroundings with their rich opportunities for culture and outdoor activities

How to apply

The application must include:

  • Cover letter (statement of motivation, summarizing scientific work and research interest)
  • CV (summarizing education, positions, pedagogical experience, administrative experience and other qualifying activity)
  • Copies of educational certificates, academic transcript of records and letters of recommendation
  • A complete list of publications and up to 5 academic works that the applicant wishes to be considered by the evaluation committee
  • 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. 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).

In assessing the applications, special emphasis will be placed on the documented academic qualifications and the quality of the project as well as the candidates’ motivation and personal suitability. Interviews with the best qualified candidates will be arranged.It is expected that the successful candidate will be able to complete the project in the course of the period of employment.

Formal regulations

Please see the guidelines and regulations for appointments to Postdoctoral fellowships at the University of Oslo.

No one can be appointed for more than one Postdoctoral Fellow 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 University of Oslo has an agreement for all employees, aiming to secure rights to research results etc.

The University of Oslo aims to achieve a balanced gender composition in the workforce and to recruit people with ethnic minority backgrounds.

Contact information

For technical questions regarding the recruitment system, please contact HR Adviser Torunn Standal Guttormsen, e-mail: [email protected]

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