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Reconstructing Deep Population History using Palaeoproteomics

This position will be focused on analyzing ancient proteomes from archaic hominids and develop models for inferring their population history during the Pleistocene. ESR13 will design their project in consultation with the advisor, but the main objective will be to use paleoproteomics data to reconstruct accurate population histories and determine the relationships among different groups of hominins, whose genomes are currently unreachable due to their age. We will combine ancient protein sequences with in silico translated protein sequences from archaic and modern hominins, so as to reconstruct the gene trees of these sequences and model the deep population history of multiple hominin groups, including population split times and admixture events. An important challenge is that the set of reconstructed gene trees across the genome will not be equivalent to the true population history of our studied hominin groups. Due to incomplete lineage sorting, hybridization and population structure, it is highly likely that a considerable proportion of the gene trees that we reconstruct will have a different topology from the topology of the true population history. We will develop methods that can account for this discordance and maximize the amount of information that can be extracted from these sequences.

The University of Copenhagen is a world-leading institution of higher learning and provides excellent PhD programs. The candidate will have the opportunity to take courses in bioinformatics, computer science, statistical inference, machine learning, data science, population genetics, paleogenomics, paleoproteomics, and archaeological science, among many others. The candidate will have to opportunity to collaborate with international leaders in the fields of paleoproteomics and paleogenomics – including Jürgen Cox, Enrico Cappellini, Frido Welker, Tomas Marques-Bonet and Kirsty Penkman – as well as other trainees that will be simultaneosuly funded by PUSHH throughout Europe.

Qualifications

The candidate should have an MSc degree or equivalent, with a background in one or more of the following areas: evolutionary biology, archaeology, population genetics, computational biology, bioinformatics, genomics, mathematics and/or statistics. Experience in proteomics is encouraged but not required for application. The ideal candidate will demonstrate working proficiency in one or more programming languages commonly used in data science (e.g. experience in Python, R, C/C++, Java or Julia) and have experience in the UNIX operating environment.

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