Postdoctoral Scholar/Researcher in Statistical Genetics – Human Genetics – Young Lab Full-time

Postdoctoral Scholar/Researcher in Statistical Genetics – Human Genetics – Young Lab

Salary range: $69,073 – $82,836

Position overview

Applications are invited for a full-time postdoctoral researcher position to work with Dr. Alexander Young on the development of mathematical theory, statistical methods, and software in statistical genetics. This role is ideal for a researcher with strong quantitative and computational skills interested in foundational research exploring how genetic variation influences human traits, diseases, behaviors, and social outcomes.

A central theme of Dr. Young's research is how we can use the randomisation of genetic material within-families to disentangle nature and nurture. This then enables us to understand how genetic and cultural transmission from parents to offspring, combined with mating patterns, shape the distribution of traits in the population. The successful candidate will play a central role in developing new statistical methods, software, and theoretical frameworks.

Dr. Young is on the steering committee member of the Social Science Genetic Association Consortium (SSGAC), a cooperative enterprise between medical researchers and social scientists that coordinates genetic association studies across the biomedical and social sciences, fostering interdisciplinary collaboration. The successful candidate will have the opportunity to collaborate with Prof. Daniel Benjamin (UCLA Human Genetics and Anderson School of Management), as well as other prominent SSGAC researchers including Patrick Turley (USC) and Aysu Okbay (Amsterdam UMC).

Key responsibilities include developing and applying mathematical and statistical models for analyzing genetic data, designing and implementing computational tools and software to support statistical genetics research, and leading and contributing to academic publications and presenting findings at scientific conferences.

Qualifications

Basic qualifications include a Ph.D. in statistics, mathematics, computational biology, statistical genetics, quantitative genetics, or a closely related field. Strong background in quantitative methods, statistical modeling, and computational approaches is required. Experience in programming (e.g., Python, R, C++, Julia) and working with large-scale genetic datasets is necessary. Demonstrated research excellence, evidenced by publications or working papers, is also required. Ability to work independently and collaboratively in an interdisciplinary environment is essential.

Application requirements

Document requirements include a curriculum vitae, cover letter, research paper or other writing sample, statement of research (optional), and reference check authorization release form. Reference requirements include two required references (contact information only). Please provide the names and contact information of two references.