How to apply for a position

Computational Biology // Massey University

How to apply for a position

Here are some hints that you should follow that will increase your chances for a successful application.

Our group is computational and we are interested in people combining statistical and computational skills with interests in biology and medicine. If you have never written a line of code in your life, you may want to consider working at a more suitable place for your skill set. However, if you really are driven to become a more quantitative person and you have a solid background in the life sciences there might still be a place for you here but you would need to up-skill on the computational side rather quickly.

Working here with us is a great chance for you to extend your skill set and get knowledgeable in computing for the life sciences.

We do receive our fair share of applications over the year and you increase your chances of getting a response rather quickly if your application is well written. There are some commonalties of well written applications: They address the group leader by name, e.g. "Dear Dr. Schmeier". Often, however, we receive emails with one variation or the other of "Dear respected sir", etc. These almost always will get deleted without a response from our side. We would also like to read in your application why you think you are a great fit for the group and how your background and interests complement the work we do. A proper CV is a must have too.

If your application is well written and you have the required background we are happy too start discussing your options.


Dr. Sebastian Schmeier
Research Group Leader
Senior Lecturer in Bioinformatics/Genomics

Massey University
Auckland, New Zealand
+64 9 414 0800 (ext: 43538)

Publications // latest

MinION Sequencing of colorectal cancer tumour microbiomes – a comparison with amplicon-based and RNA-Sequencing. PLoS One, 2020, accepted.

Molecular subtyping improves prognostication of Stage 2 colorectal cancer. BMC Cancer, 2019, 19, 1155

DeePEL: Deep learning architecture to recognize p-lncRNA and e-lncRNA promoters. In proceedings: IEEE International Conference on Bioinformatics and Biomedicine, 2019, B516, accepted.

News&Blog // latest

[ 20190528 | news ] Recent funding successes.

[ 20190319 | news ] New publication: Frontiers in Immonology

[ 20190122 | news ] New publication: BMC Genomics

[ 20181230 | news ] New publication: Molecular Phylogenetics and Evolution

[ 20180703 | news ] New publication: Nature Methods

Tweets // latest