About the position
Applications are invited for a researcher position at the Oslo Centre for Biostatistics and Epidemiology (OCBE, University of Oslo). The candidate will be part of the research group “Statistical learning in molecular medicine” led by Professor Manuela Zucknick.
They will work on the development and application of statistical and machine learning methods for predicting outcomes and identifying biomarkers based on diverse sources of molecular data, often in multi-response settings with heterogeneous samples and multi-modal input data. A key application area will be the analysis of large-scale screens of in vitro assay data, e.g. cancer pharmacogenomic screens and immuno-oncology cell-based assay screens.
The candidate will be heavily involved in the ongoing Horizon2020 IMI2 project “IMMUNE SAFETY AVATAR – Nonclinical mimicking of the immune system effects of immunomodulatory therapies” (ImSAVAR) funded by the European Union, in particular in working package 4, which is concerned with the identification of non-clinical and clinical biomarkers for adverse immune reactions to cancer therapies. The position is founded for 12 months.
About the research group
The research group “Statistical learning in molecular medicine” is part of the Oslo Centre for Biostatistics and Epidemiology (OCBE). OCBE is one of Europe’s most active biostatistics groups with currently ca 75 researchers. OCBE is internationally recognized, with interests spanning a broad range of areas – including statistical machine learning, high-dimensional data and big data, computationally intensive inference for complex models, causal inference and survival models, measurement uncertainty, and research for clinical trials and observational studies – and numerous collaborations with leading bio-medical research groups internationally and in Norway. OCBE is a key partner of the Norwegian Centre for Knowledge-driven Machine Learning “Integreat”, a recently established Centre of Excellence funded by the Research Council of Norway.
Main tasks for the position
The candidate will participate in the design and implementation of studies, development and implementation of statistical methods for the analyses of high-dimensional omics data and large-scale in vitro assay data, preparation of manuscripts and communication of research findings. We provide an exciting, progressive and friendly research environment to develop novel statistical machine learning methodologies and apply them in cutting edge biomedical research projects in a collaborative and interdisciplinary fashion, nationally and internationally. The candidate will collaborate with other members of the imSAVAR EU project and participate in regular project meetings. The candidate will work closely with the “Statistical learning in molecular medicine” group at OCBE, UiO. Ability to work in a multi-disciplinary team is essential.
The candidate will work on the development and application of statistical and machine learning methods for predicting outcomes and identifying biomarkers based on diverse sources of molecular data. A key application area will be the analysis of large-scale screens of in vitro assay data, e.g. cancer pharmacogenomic screens and immuno-oncology cell-based assay screens. For example, a main task in the imSAVAR project will be the analysis of immuno-oncology cell-based assay data, in particular a large screen with T-cell engagers cytokine release assays (TCE-CRA).
This requires development and application of statistical learning methods suitable for multi-response regression setups with multi-
modal heterogeneous input data and asymmetric interaction terms to deal with sample heterogeneity, in particular pliable lasso methods.
Qualifications of the researcher:
Applicants must hold a degree equivalent to a Norwegian doctoral degree in statistics or applied mathematics. Doctoral dissertation must be submitted for evaluation by the closing date. Appointment is dependent on the public defense of the doctoral thesis being approved.
A PhD in statistics or applied mathematics is required, with experience and training in computational statistics and statistical machine learning. Experience with methods for multi-response regression with multi-modal input data, sample heterogeneity and interaction terms is a strong advantage.
Strong quantitative and computational skills. Fluency in R or equivalent statistical/ machine learning software and experience in developing statistical software packages is required.
Experience with biostatistical applications in molecular medicine is required, especially with analysis of high-dimensional omics data and large-scale in vitro assay data.
Candidates must also have excellent interpersonal and communication skills.
Personal suitability will be emphasized.
Working language in the group and at OCBE is English, hence fluency in English is required.
We can offer:
Full time position
An exciting research environment with a multidisciplinary profile and good opportunities for academic development
Strong national and international collaborators
Salary according to current agreements
Favorable pension arrangements
Salary NOK 584 500- 635 400 per annum depending on qualifications in position
To be considered, please complete the online application including a detailed cover letter, CV and three professional references. The cover letter should include:
1. Description of the candidate’s experience and training in computational statistics and statistical machine learning (please list competencies in advanced statistical and machine learning techniques)
2. Documentation of the candidate’s experience with writing statistical software
3.Description of the candidate’s experience with biostatistical applications in molecular medicine, especially with analysis of high-dimensional omics data and large-scale in vitro assay data
4.Three references with phone numbers, emails and addresses
5. 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 as well as the candidate’s motivation and personal suitability. Interviews with the best qualified candidates will be arranged.
Contactinformation
Manuela Zucknick, manuela.zucknick@medisin.uio.no
