Postdoc in Computational Biology/Bioinformatics at BRIC

at  KU SCIENCE BIO BRIC

UNIVERSITY OF COPENHAGEN

We are looking for a highly motivated and dynamic computational postdoc for a 3-year position, to join the group of Prof. Ana Cvejic from 1 February 2025 or soon thereafter.
Information on the department can be found at: BRIC – University of Copenhagen (ku.dk)

YOUR JOB

In this project you will analyse newly generated scRNA-Seq and 10X multiome data sets to reveal the molecular programmes that are active in human blood cells. You will employ cutting-edge single-cell computational approaches to chart and understand the gene regulatory networks that are active in blood cells in health and disease.
The successful candidate will have a PhD in Bioinformatics, Computational Biology, Biostatistics or in a related quantitative field (e.g., Statistics, Mathematics, Physics). You will be passionate about science, hardworking, and excited to learn and improve skills in an outstanding research environment. You will be part of a multidisciplinary team and will be tasked with proactively seeking and maintaining appropriate collaborations. Your task would be to drive forward the project, working closely with biologists, clinicians and bioinformaticians within and outside the group to accomplish scientific objectives.

PROFILE

We are looking for a highly motivated and enthusiastic scientist with the following competencies and experience:

Essential experience and skills:

• You have a PhD in Bioinformatics, Computational Biology, Biostatistics or in a related quantitative field (e.g., Statistics, Mathematics, Physics), and a passion for problem solving
• You are highly experienced in a research environment with a well-established publication track record
• You have formal training in mathematical, statistical, and machine-learning-based analysis of complex data sets, such as dimensionality reduction, hypothesis testing, clustering, etc.
• Experience with atlas-scale single-cell data analysis, such as cell type annotation, differential abundance/accessibility/expression testing, trajectory inference, etc.
• An understanding, experience and published outcomes from analysing and interpreting large datasets using statistical programming languages (e.g. R, Python etc.)
• Proven independent working style, technical problem solving, data analysis and generation of novel ideas
• Proficient communication skills and ability to work in a team
• Comfortable generating and documenting reproducible analyses and workflows
• Excellent English skills written and spoken

Desirable experience and skills:

• Training in statistical methods appropriate for single-cell biological research, e.g., sparse data analysis, generative models, batch correction and multi-sample/-modal data integration, etc.
• Experience extending and fine-tuning DNN models for novel tasks
• Experience with a workflow management system, e.g., Snakemake, nextflow
• Experience in statistical genomics approaches and in method development
• Experience in analysing CITE-seq datasets

Please refer the Job description for details