Research Assistant/Associate Professor (Breeding & Genetics)

About the role

The College of Agriculture, Food and Natural Resources (CAFNR) at Prairie View A&M University (PVAMU) is inviting applications for a 9-month, non-tenure track Research Assistant/Associate Professor position in Quantitative Genetics, with a focus on small ruminant genetics or data science.

Responsibilities

• Develops and implements an independent research program focused on quantitative genetics, genomic analysis, breeding systems, and data science applications in small ruminants, particularly goats.
• Contributes to strategic growth, research initiatives, herd improvement objectives, and overall advancement of the International Goat Research Center mission and infrastructure.
• Collaborates with CAFNR research and extension faculty across disciplines including ruminant nutrition, reproduction, parasitology, food science, and related agricultural sciences.
• Analyzes genetic and production data, prepares reports, publishes findings, and contributes to scientific presentations and dissemination activities.
• Participates in university compliance activities, committee service, training requirements, and other assigned duties supporting departmental and institutional operations.

Requirements

• Ph.D. in Animal Breeding, Statistical Genetics, Computational Biology, Genomics, or Data Science.
• No prior experience required.
• Experience in mentoring and advising students.

Qualifications

• Proven potential for securing extramural funding.
• Demonstrated ability to publish research in peer-reviewed journals.
• Excellent written and oral communication skills, along with strong organizational and interpersonal abilities.
• Postdoctoral experience in animal breeding or related fields.

Preferred Qualifications

• At least five years of research experience in breeding, genetics, or data science.
• Demonstrated ability to collaborate effectively and participate in interdisciplinary research.
• Prominent experience in grant writing and leading research projects.
• Record of peer-reviewed publications in breeding, genetics, or data science.
• Experience in teaching undergraduate and graduate courses.
• Background in livestock and small ruminant research.