Tag: Machine learning

Erik Kristiansson, who was co-supervisor for my PhD thesis, has an opening for a PhD student funded by the DDLS program. The project is combining bioinformatics and artificial intelligence with a focus on large-scale data analysis to better understand antibiotic resistance and the emergence of novel resistance genes. The research will be centered on DNA sequence analysis, inference in biological networks, and modelling of evolution. The primary applications will be related to antibiotic resistance and bacterial genomics.

I am particularly excited about this position because I will have the benefit of co-supervising the student. The student will also be part of the DDLS research school which is now being launched, which is also super-exciting for Swedish data driven life science.

The candidate is expected to have a degree in bioinformatics, mathematical statistics, mathematics, computer science, physics, molecular biology, or any equivalent topic. Previous experience in analysis of large-scale biological data is desirable. It is important to have good computing and programming skills (e.g. in Python and R), experience with the Linux/UNIX computer environment, and, to the extent possible, previous experience in working with machine learning and/or artificial intelligence.

I had such a good time with Erik as my co-supervisor, and he has put together a truly amazing supervision team with Joakim Larsson, Anna Johnning and myself. I could not imagine a better place to apply bioinformatics and ML/AI on antibiotic resistance! Deadline is June 7! Application link here: https://www.chalmers.se/om-chalmers/arbeta-hos-oss/lediga-tjanster/?rmpage=job&rmjob=12840&rmlang=SE

I am very happy to welcome Agata Marchi back to the group as a PhD student! Agata was a master student in the group last year, doing a thesis focused on implementing a bioinformatic approach to identify differences between the genomes of host-associated and non host-associated strains of Pseudomonas aeruginosa. While one of her first tasks will be to complete this work and prepare it for publication, her doctoral studies will primarily be on the interactions between bacteria and between bacteria and host in the human microbiome and how these relate to complex diseases. She will focus on developing and applying machine learning methods to better understand this interplay.

I am – as the rest of the group – very happy to welcome Agata back to the lab!