PhD Candidate: Marine Population Genetics & Modeling

Alexander Skomakerstuen; Trondheim; On-site; NOK 420,000 - 540,000; Full time

Job summary

NTNU's Department of Biology invites applications for a fully-funded PhD position focusing on modelling marine larval dispersal and population genetics. The project blends ecology, oceanography, and computational biology, with supervision by Associate Professor Scott Burgess.

The role offers broad training and opportunities for international collaboration and conferences. As a PhD Candidate, you will develop independent research lines within this scope, work with simulations, machine learning

Qualifications

• Strong background in ecology or evolution and quantitative methods.
• Experience with population genetics analyses and genetic data.
• Good oral and written English communication skills.

Responsibilities

• Complete doctoral education by defending a PhD.
• Apply advanced statistical and computational methods to analyze genetic data.
• Contribute to reproducible code and workflows.
• Produce a high-quality PhD thesis for international journals.
• Participate in conferences and research stays at international institutions.
• Deliver presentations for research dissemination.
• Collaborate within a local and international research team.
• Undertake teaching and other professional activities as agreed.

Skills

Statistical analysis

Programming

English proficiency

Population genetics

Education

Master's degree

Five-year Norwegian degree with master thesis

Tools

SLiM

Linux

Bash

Git

Job description

NTNU's Department of Biology invites applications for a fully-funded PhD position focusing on modelling marine larval dispersal and population genetics. The project blends ecology, oceanography, and computational biology, with supervision by Associate Professor Scott Burgess.

The role offers broad training and opportunities for international collaboration and conferences. As a PhD Candidate, you will develop independent research lines within this scope, work with simulations, machine learning