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
Faculty of Mathematics and Natural SciencesProject 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.
Although not required, the following qualifications will be an advantage in the assessment of the applicants:
The norm is as follows:
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
The application must include:
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 2023Employer
University of OsloMunicipality
Fulltime (1 positions) Fulltime (%)Duration
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