Postdoctoral Fellowship-Marignani Discovery Research Laboratory

The Marignani Lab in the Faculty of Medicine at Dalhousie University, Halifax, Nova Scotia, Canada is seeking outstanding candidates for postdoctoral research in multi-omics with an emphasis on bioinformatics and integrative analysis pipelines associated with scRNA-seq, snRNA-seq, snATAC-seq and HTP screening platforms. The Marignani Lab is committed to equity, diversity and inclusion practices to ensure academic and professional development of our trainees. Dalhousie University sits on Mi’kma’ki, the ancestral and unceded territory of the Mi’kmaq People. We are all Treaty People.

About You:

You will have a PhD in Bioinformatics, Computational Biology, Biostatistics or in a related quantitative field (e.g. Mathematics, Physics, Statistics). You will be passionate about science, hardworking, and excited to learn and improve your skills in an outstanding and supportive research environment. You will be part of a multidisciplinary team and will be tasked with proactively seeking and maintaining appropriate collaborations. You will also be expected to drive forward the project, working closely with biologists, clinicians and bioinformaticians within and outside the group to accomplish scientific objectives.

  • Recent PhD in computational biology, computer science, biostatistics or in related quantitative fields (Mathematics, Physics, Statistics).
  • Proven track record in computational science; hands-on experience in bioinformatics applied to next-generation sequencing data, and ideally to single-cell sequencing and/or long-read technologies.
  • Strength in written and oral use of the English language

Essential Skills Technical:

  • Extensive experience in a research environment with a publication track record in computational research fields and/or single-cell RNA-seq (sc/snRNA-seq).
  • Formal and extensive training in mathematical and statistical analysis of complex data sets
  • An understanding, experience and published outcomes from analysing and interpreting large datasets using at least one statistical package (e.g. R/SPLUS, SAS) and experience in programming (e.g. Perl, Python, C++, Java).
  • Python experience is an advantage.

Ideal Skills:

  • Training in statistical methods appropriate for biological research
  • Experience in genomics and transcriptomics approaches (phylogenetics, SNP analysis, single-cell RNA-sequencing), and in method development
  • Experience in implementation of machine learning techniques (e.g. linear/non-linear classification, ensemble learning, neural networks, Bayesian/variational inference/MCMC, clustering and dimensionality reduction)
  • Experience in single-cell multi-omics analysis in Python and/or R e.g. analysis with ScanPy in Python and/or Seurat in R

Apply by sending the following information to Dr. Marignani

  • Cover letter
  • Equity, Diversity, Inclusion statement
  • complete Curriculum vitae
  • Attached relevant publications
  • Names, and email information of thee references.