Postdoctoral position in Machine Learning and Computational Biology
Nordland fylkeskommune Se alle jobber
- Oslo
- Midlertidlig
- Fulltid
- Analyze quantitative molecular and imaging data produced by other members of GENESIS.
- Develop new machine learning models to identify gene regulatory mechanisms involved in the physical mechanisms of cellular self-organization during early mouse embryo.
- Develop new machine learning models to learn minimal dynamic models of gene regulation from longitudinal molecular and imaging data.
- Work closely with other members of GENESIS performing biological experiments, quantitative imaging and biophysical modeling to develop a unified multiscale framework connecting gene regulation, tissue mechanics and morphogenesis.
investigates how genetic factors integrate with active matter principles to drive the self-organization of the mammalian early embryo. Through single-cell transcriptomics, spatially resolved gene expression, advanced live microscopy, and modeling of active matter principles, we aim to uncover the connections between gene regulation and active matter in the earliest developmental stages of mammals.
The project is led by Professor Arne Klungland, the Director of the Center of Excellence CRESCO: Centre for Embryology and Healthy Development at UiO together with Professor Luiza Angheluta-Bauer from the Department of Physics at UiO, Group Leader Stig Ove Bøe from the Department of Microbiology at Oslo University Hospital and Associate Professor Alvaro Köhn-Luque from OCBE, Department of Biostatistics, University of Oslo.About OCBE
is one of Europe's most active biostatistics groups with currently about 75 researchers. OCBE is internationally recognized, with interests spanning a broad range of areas, including high dimensional statistics, machine learning, mathematical modelling, causal inference, time-to-event analysis, clinical trials, infectious disease and epidemiology, The centre has numerous collaborations with leading biomedical research groups internationally and in Norway.About Integreat
is a Centre of Excellence funded by the Research Council of Norway of the University of Oslo, UiT the Arctic University of Norway and the Norwegian Computing Center. Our research makes machine learning more sustainable, accurate, trustworthy, and ethical.About the Life Science Building
will be a shared facility for leading university and hospital environments within life sciences and is intended to ensure Norway's international competitiveness in the field. Here, students and various academic communities will work interdisciplinarily to develop new solutions to major challenges in health and sustainability, with access to the best and most modern equipment required to conduct world-class research and innovation.Qualification requirementsRequired qualifications:
- PhD in machine learning, statistics, computational biology, applied mathematics, physics, or similar. Eligible candidates may apply before completing their PhD degree. However, a documented proof of a PhD degree is required upon appointment
- Excellent programming skills (Python, Julia, R) and maintenance of code repositories
- Proven experience in machine learning method development
- Fluency in English
- Experience analyzing high-dimensional data, in particular single cell transcriptomics/epigenomic and quantitative imaging data
- Experience in computational modeling of gene regulation and morphogenesis
- Experience working on high-performance computing environments
- Exciting and meaningful tasks in an organization with an important societal mission, contributing to knowledge development, education, and enlightenment that promote sustainable, fair, and knowledge-based societal development.
- A pleasant and stimulating work environment
- Good
- Opportunity of up to 1.5 hours a week of
- A workplace with good development and career opportunities.
- Membership in the
- Cover letter - statement of motivation and research interests
- CV (summarizing education, work experience and academic work, including any scientific publications and theses, and other qualifying activity)
- Transcripts of records, copies of the original bachelor’s, master’s and PhD degree diploma
- Documentation of English proficiency if applicable
- List of publications and academic work 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)