Three Scientia Fellow postdocs in Statistics, Biostatistics, Data Science and Biomathematics offered at the University of Oslo (UiO), three years appointments.
Call: https://www.med.uio.no/english/research/scientia-fellows/apply/
Deadline: 20. August 2019
Pre-deadline (obligatory): 10. July 2019
SCIENTIA FELLOWS is a transnational research fellowship programme in the field of Health Life Sciences of the Faculty of Medicine at the University of Oslo. It is partly funded by the EU Horizon 2020 under the Maria Skłodowska-Curie scheme. SCIENTIA FELLOWS is committed to support postdoctoral researchers by providing them with excellent research environments and opportunities for career development.
The purpose of the three years postdoc is to produce scientific results of the highest quality, in collaboration with your host at UiO and its other partners. You will be part of UiO’s career development programme designed for researchers at the beginning of their career. You can also join a course in Health Innovation which aims to provide researchers with tools and insight into how innovation can be put to work for the benefit of patients, the healthcare system and our society. At completion of your three year term, your academic profile will be such that you can successfully apply to the best research positions worldwide.
Eligibility:
• You possess a PhD degree (at the latest by 1 November 2019) in statistics, biostatistics, mathematics, computer science or other related disciplines with a documented competence in statistics, biostatistics or mathematics.
• You have not been resident in Norway for more than 12 months in the last 3 years.
You apply for each position separately, for just one or more. Mention clearly which position you are applying for.
All three positions will be at the Oslo Center for Biostatistics and Epidemiology (OCBE), UiO, and in collaboration with the Oslo University Hospital. This is an opportunity to join one of Europe’s most active biostatistics groups at an exciting time. Currently, OCBE has eight tenured professors, four tenured associate professors, fifteen tenured researchers, post-doctoral fellows and PhD students, making up a group of about 80 scientists. OCBE is internationally recognized, with interests spanning a broad range of areas (including time-to-event models, data integration, causal inference, statistical genomics, Bayesian inference, informative missingness and measurement error models, theory and practice in machine learning, epidemiological studies of lifestyle and chronic diseases, stochastic models for infectious diseases, high dimensional data and models), and numerous collaborations with leading bio-medical research groups nationally and internationally. OCBE has a leading role in the center for research-based innovation BigInsight, it hosts the ERC Advanced Grant of Professor Corander, and several further important projects in the areas of statistical methods for biobank and digital life sciences. OCBE is responsible for the biostatistical teaching for the professional study in medicine and for the PhD training. OCBE also provides an extensive advisory service in data science and biostatistics for biomedical and clinical researchers at UiO and the Oslo University Hospital.
In order to apply for the position, you have to prepare a research proposal. You and your UiO host must identify common research interests and discuss the concept of your research proposal. While you are responsible for the research proposal, you will discuss your application via Skype and email. Your host is encouraged to help you to write a good proposal.
Please start by contacting your host (see below) by sending your expression of interest in the position, your resume and links to your papers by July 10th.
As part of your application, your host will issue a statement approving and supporting your application. Please follow carefully all rules of this call, which are special and well described in https://www.med.uio.no/english/research/scientia-fellows/apply/.
Applications for each position will be evaluated by independent international experts. Candidates will be evaluated based on: excellence (of the candidate and proposal), implementation (of the project) and impact (of the fellowship on the applicant’s career prospects and on scientific innovation).
A Fellow of the Scientia Fellows programme will be employed at UiO for three years. The place of work is the UiO campus in Oslo. The gross salary of a Fellow will amount to 515 200 NOK/year. UiO will cover full health insurance and pay towards your pension with the Norwegian pension fund. As employee in Norway you have several welfare benefits. In addition UiO will support research costs (laptop, travel, courses etc) with 54 600 NOK per year. For further information related to moving and settling in Norway, please visit the website of the International Staff Mobility Office (https://www.uio.no/english/about/jobs/ismo/), which will also assist incoming fellows and their families with relocation to UiO.
For more information, contact the hosts mentioned below.
Postdoc positions offered:
1. Biostatistics for personalised cancer therapy
https://www.med.uio.no/english/research/scientia-fellows/thematic-areas/biostatistics-for-personalized-cancer-therapy/index.html
Host: Manuela Zucknick, https://www.med.uio.no/imb/english/people/aca/manuelkz/
email: manuela.zucknick@medisin.uio.no
This postdoc will develop innovative methods in statistics and machine learning for personalised cancer therapy. This research is organised through the UiO:LifeScience research environment PERCATHE and the research group at UiO and the Oslo University Hospital includes about 30 researchers, including Arnoldo Frigessi (https://www.med.uio.no/imb/english/people/aca/frigessi/), Eivind Hovig (https://ous-research.no/home/hovig/Staff/2326) and Kjetil Tasken (https://www.med.uio.no/klinmed/english/people/aca/ktasken/index.html ).
Theme for this research project: Estimating and predicting cancer drug sensitivity in in-vitro screening We run a large project on drug sensitivity estimation and prediction. The aim is to be able to guide the selection of cancer therapy based on the statistical prediction of how drugs will behave for the individual patient, each drug on its own and in combination, by modelling synergistic effects. We will develop new multivariate penalized and Bayesian methods to improve prediction of drug sensitivity in large-scale screening experiments based on molecular characterization of cancer cell lines and patient samples as well as properties of the drugs. One particular challenge is the integration of multiple heterogeneous data sources, for example via multiple kernel learning.
Two relevant references:
• Ickstadt K, Schäfer M, Zucknick M (2018). Toward Integrative Bayesian Analysis in Molecular Biology. Annual Review of Statistics and Its Application. 5:141-167.
• Menden MP, et al. (2018). A cancer pharmacogenomic screen powering crowd-sourced advancement of drug combination prediction. bioRxiv, 200451 (accepted in Nature Communications).
2. Biostatistics and biomathematics
https://www.med.uio.no/english/research/scientia-fellows/thematic-areas/biostatistics-and-biomathematics/index.html
Host: Magne Thoresen, https://www.med.uio.no/imb/english/people/aca/magnet/index.html
email: magne.thoresen@medisin.uio.no
This postdoc will develop innovative methods in statistics, machine learning, mathematics or data science for high dimensional health data. The research group at UiO includes about 10 researchers, including Arnoldo Frigessi (https://www.med.uio.no/imb/english/people/aca/frigessi/). This postdoc is part of the centre for research-based innovation BigInsight (https://www.biginsight.no/), a consortium of academic, industrial and public partners, with a funding of about 4 million Euro annually until 2023. BigInsight develops original model-based statistical and machine learning methodologies and analytical and computational tools to extract knowledge from complex and big data. BigInsight’s partner in this project will be the Oslo University Hospital.
Theme for this research project: Statistical methods for high-dimensional data analysis This project will focus on high-dimensional data analysis from a methodological point of view, when integrating many different sources of high-dimensional data. Methods and themes for the project might include, among others, penalized regressions other dimension reduction techniques, high-dimensional mixed models, high-dimensional mediation analysis, measurement error in high-dimensional regression and testing in high-dimensional situations. The postdoc project should relate to one or more of these topics, motivated by applications in biomedicine and/or public health, as for example in https://www.biginsight.no/s/BigInsight_annual_report2018.pdf.
Two relevant references:
• Djordjilovic V. et al. (2019). Global test for high-dimensional mediation: Testing groups of potential mediators. Statistics in Medicine; https://doi.org/10.1002/sim.8199.
• Feng Q. et al. (2018). Angle-based joint and individual variation explained. Journal of Multivariate Analysis 166: 241-265.
3. Biostatistics and health data science
https://www.med.uio.no/english/research/scientia-fellows/thematic-areas/biostatistics-and-health-data-science/index.html
Host: Arnoldo Frigessi, https://www.med.uio.no/imb/english/people/aca/frigessi/
email: frigessi@medisin.uio.no
This postdoc will develop innovative network-centric and machine learning-based methods to modelling and predicting complex relationships between genetic dependencies and bio-medical phenotypes. The project is led by Professor Tero Aittokallio at UiO and the Oslo University Hospital (https://www.fimm.fi/en/research/groups/aittokallio) jointly with Arnoldo Frigessi.
Theme for this research project: Computational systems medicine approaches for analysing, modelling and integration of large-scale datasets This project develops and applies computational systems medicine approaches for analysing, modelling and integration of large-scale drug testing and molecular profiling datasets. In a multidisciplinary research environment, the aim is to implement computationally efficient statistical and machine learning models for mining combinations of molecular and clinical features most predictive of individual medical outcomes and differences in treatment responses, which may eventually provide predictive biomarkers for clinical translation.
Two relevant references:
• Guinney et al. Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data. Lancet Oncol. 2017 Jan;18(1):132-142.
• He L, Tang J, Andersson EI, Timonen S, Koschmieder S, Wennerberg K, Mustjoki S, Aittokallio T. Patient-Customized Drug Combination Prediction and Testing for T-cell Prolymphocytic Leukemia Patients. Cancer Res. 2018 May 1;78(9):2407-2418.