PhD Research Fellow in Informatics/Bioinformatics

PhD Research Fellow in Informatics/Bioinformatics About the position

PhD Research Fellowship in Informatics/Bioinformatics with emphasis on synthetic data generation, applied machine learning and benchmarking is available at the Centre for Bioinformatics (SBI) hosted at the Department of Informatics, Faculty of Mathematics and Natural Sciences, University of Oslo.

The fellowship will be a full-time position for a period of four (4) years with 25% compulsory work within Computational Life Sciences. The starting date can be between June and September 2023 and can be no later than September 1st 2023.

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

Knowledge development in a changing world - Science and technology towards

2030

Faculty of Mathematics and Natural Sciences

Project description

The PhD position is one of four PhD and post-doc positions within the UiORealArt Convergence environment at the University of Oslo. Convergence environments are interdisciplinary research groups that will aim to solve grand challenges related to health and the environment. They are funded by UiO's interdisciplinary strategic area UiO:Life Science www. uio.no/life- science.

The aim of this Convergence environment is to improve causal inference in perinatal pharmaco-epidemiology using machine learning approaches on real- world and artificial data” (UiORealArt, 2022-2026). The project unites expertise within natural sciences and medicine (pharmacology, machine learning, bioinformatics, statistics, genetics, and epigenetics) with social/educational sciences (psychology, language development and educational attainment).

Perinatal pharmaco-epidemiology refers to a field that studies the safety of medication use during pregnancy. Causal inference in perinatal pharmaco- epidemiology is extremely challenging because of the nature of allowed data collection procedures while the methodological progress has on some aspects stagnated. There is currently a surge of interest in applying machine learning (ML) to epidemiological questions under the assumption that it would improve causal inference. There has also been an increased interest in incorporating high-dimensional molecular data together with epidemiological data from national health registries to strengthen causal inference. However, both the application of ML and the incorporation of high-dimensional molecular data appear to have been mostly based on hype and popular trends rather than a deeper appreciation for problem characteristics and suited inductive biases. The PhD candidate will build on the combined expertise of epidemiologists and machine learners involved in RealArt and will work at the intersection of bioinformatics, machine learning and causal inference. First, while causality has traditionally been focused on linear relations involving data of limited size/dimensionality, there is currently an increasing interest in considering higher complexity (non-linear) relations and using data of larger size and higher dimensionality. Second, while machine learning has traditionally been focused on reflecting statistical associations in fixed underlying distributions – based on a dataset assumed to be an iid representation of this underlying distribution – there has lately been an increased realization that real-world application of machine learning very often involves substantial domain shifts, and that causality provides perspectives that are very useful to reason about and to improve the generalizability/robustness of ML models in face of such challenges.

The PhD student will mainly lead projects related to synthetic data generation, applied machine learning and benchmarking within the scope of RealArt. The following are provisional project directions in sequential order: 1) develop a framework for simulating high-dimensional molecular marker datasets under a defined causal structure with varying levels of correlations. 2) develop a benchmark suite to enable the assessment of the feasibility of drawing causal conclusions through the incorporation of molecular markers in perinatal epidemiology. 3) Assessment of the performance of analytical methods in recovering the ground truth associations between molecular markers and covariates in perinatal epidemiology datasets.

The candidate will become affiliated to the Centre for Bioinformatics hosted at the Department of Informatics, Faculty of Mathematics and Natural Sciences, University of Oslo, where Chakravarthi Kanduri will be the main supervisor. The PhD student will be co-supervised by Kristina Gervin, Div of Clinical Neuroscience, OUS, UiO. The work will be performed in close collaboration with Professor Geir Kjetil Sandve, Dpt. of Informatics and Professor Hedvig Nordeng, Dpt. Pharmacy, Faculty of Mathematics and Natural Sciences, UiO, the UiORealArt members and international partners.

The main purpose of the fellowship is research training leading to the successful completion of a PhD degree.

Qualification requirements

The Faculty of Mathematics and Natural Sciences has a strategic ambition to be among Europe's leading communities for research, education and innovation. Candidates for this fellowship will be selected in accordance with this, and expected to be in the upper segment of their class with respect to academic credentials.

Required qualifications:

Applicants must hold a Master's degree equivalent to a Norwegian Master's degree in Bioinformatics/Informatics, Data Science, Computer Science, Statistics or a related field. Candidates without a Master's degree have until 30 June 2023 to complete the final exam.

Strong programming skills in Python

Excellent communication skills in English, both oral and written

Desired qualifications:

Although not required, the following qualifications will be an advantage in the assessment of the applicants:

Documented experience and/or decent grasp of machine learning or statistical methodologies

Documented experience in handling data from high-throughput molecular data (omics)

Projects or coursework in causal inference

Projects or coursework involving data simulations

Grade requirements:

The norm is as follows:

the average grade point for courses included in the Bachelor's degree must be C or better in the Norwegian educational system

the average grade point for courses included in the Master's degree must be B or better in the Norwegian educational system

the Master's thesis must have the grade B or better in the Norwegian educational system

Fluent oral and written communication skills in English

English requirements for applicants from outside of EU/ EEA countries and exemptions from the requirements:

https: // www. mn.uio.no/english/research/phd/regulations/regulations.html#toc8

The purpose of the fellowship is research training leading to the successful completion of a PhD degree.

The fellowship requires admission to the PhD programme at the Faculty of Mathematics and Natural Sciences. The application to the PhD programme must be submitted to the department no later than two months after taking up the position. For more information see:

https:// www. uio.no/english/research/phd/

https:// www. mn.uio.no/english/research/phd/

Personal skills

Ability to work independently as well as in multidisciplinary teams

Ability to give and receive constructive scientific criticism

Resourceful, structured, and result-oriented.

We offer

Salary NOK 501 200 – 544 400 per year depending on qualifications and seniority as PhD Research Fellow (position code 1017)

Attractive welfare benefits and a generous pension agreement

Vibrant international academic environment

Career 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: The candidates are advised to describe in the cover letter how they meet the required qualifications and if they meet any of the desirable qualifications. The cover letter should also include a statement of motivation, research interest and short summary of any prior scientific work.

CV (summarizing education, skills, any previous positions, and other qualifying activities such as teaching experience, and administrative experience)

Copies of educational certificates (original Bachelor and Master's degree diplomas), academic transcript of records and letters of recommendation

Any examples of code from the applicant's previous work shared through a GitHub link that allows the evaluation of programming skills

List of publications (if any) and academic work (e.g., master's thesis or similar) 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, please follow the link “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).

In assessing the applications, special emphasis will be placed on the documented, academic qualifications, the statement of motivation and the candidate's 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 Research Fellowships at the University of Oslo.

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 University of Oslo has an agreement for all employees, aiming to secure rights to research results etc.

Inclusion and diversity are a strength. The University of Oslo has a personnel policy objective of achieving a balanced gender composition. Furthermore, we want employees with diverse professional expertise, life experience and perspectives.

If there are qualified applicants with disabilities, employment gaps or immigrant background, we will invite at least one applicant from each of these categories to an interview.

Contact information

For further information please contact: Chakravarthi Kanduri, e-mail: [email protected] or Kristina Gervin e-mail: [email protected]

For questions regarding Jobbnorge, please contact HR Adviser Therese Ringvold, e-mail: [email protected]

About the University of 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.

Deadline

10th March 2023

Employer

University of Oslo

Municipality

Oslo

Scope

Fulltime (1 positions) Fulltime (%)

Duration

Engagement

Place of service