Developing Machine learning tools to date Viking genomes and identify protective genes in plants. Candidates are expected to have an interest in biology, human history and plants alongside strong computational skills with background in mathematics, statistics, physics, computer science, and/or a related field. Candidates are also expected to have a fundamental knowledge and experience with Machine Learning methods. The candidate will work jointly with Dr. Eran Elhaik and Prof. Allan Rasmusson, Department of Biology, Lund University, and Prof. Laura Grenville-Briggs Didymus (at the Swedish University of Agricultural Sciences, (SLU), Alnarp, to develop statistical methods for two different projects, one in population genetics and one in functional genomic.
In the human population genetics project, we aim to develop an ML method that compares the similarity of ancient genomes to predict their age with the goal of dating ancient Viking genomes. In the plant genomics project, we are interested in developing an optimized system of using DNA and RNA NGS and mapping data to identify genes associated with how a crop plant responds to a biotic treatment for biocontrol and biostimulation. This is a multi-disciplinary project involving programming and modelling. In addition, the project will involve collaborations with researchers in other disciplines, including biomathematics, biostatistics, and molecular biology. The candidate is expected to have a strong grounding in programming in R and math/statistics.
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