Description
The Department of Animal Sciences at the University of Arkansas is currently seeking a high-caliber doctoral student for a fully funded research position starting in Fall 2026. This role offers an immersive experience in the burgeoning field of precision medicine, specifically tailored to improving animal health through data-driven insights. Under the expert mentorship of Dr. Aranyak Goswami, the successful candidate will bridge the gap between theoretical data science and practical biological applications, utilizing some of the most advanced computational resources available in academia today.
Position Overview
This doctoral opportunity focuses on the development of sophisticated computational pipelines to decode the complex relationships between host genetics and environmental factors. By integrating multi-omic data, the researcher will aim to identify novel biomarkers and establish causal links within disease pathways, contributing to the next generation of animal health interventions.
- Mentorship: You will work directly under Dr. Aranyak Goswami within the Department of Animal Sciences.
- Research Focus: Developing computational pipelines to integrate host genetics with multi-omic data.
- Objective: Identifying novel biomarkers and establishing causal disease relationships in the context of precision medicine.
Required Qualifications
To maintain the high standards of the program, candidates must meet rigorous academic and legal criteria. This recruitment cycle specifically prioritizes candidates who are already established within the United States academic system, ensuring a seamless transition into the intensive research environment of the Goswami Lab.
- Education: Must hold a Master’s degree in Bioinformatics, Computational Biology, Genetics, Molecular Biology, or a related quantitative field.
- Academic Performance: A minimum GPA of 3.5 from a U.S. Master’s program is required.
- Standardized Tests: Submission of valid GRE and/or SAT scores is mandatory.
- Visa Status: Applicants must currently reside in the USA with a valid F-1 or J-1 student visa.
Desired Skills & Responsibilities
The ideal candidate will possess a robust technical foundation, spanning from traditional population genetics to modern data engineering. The role requires a "full-stack" approach to genomic data, where the student is responsible for everything from the initial data cleaning to the final statistical inference.
- Statistical Genomics: Proficiency in executing Microbiome Genome-Wide Association Studies (mGWAS) and population genetics approaches like Heritability Estimation (GREML) and Genetic Correlation analysis.
- Causal Inference: Familiarity with Mendelian Randomization (MR) to establish causal links between biological profiles and host phenotypes.
- Programming: Fluency in Python and R for statistical modeling and development within a Linux/Unix environment.
- Data Engineering: Proficiency in managing end-to-end ETL processes, data wrangling, and cleaning of large-scale genomic datasets.
Preferred Qualifications
Beyond the core requirements, the lab is interested in candidates who can leverage cutting-edge technologies to solve biological puzzles. Expertise in deep learning and high-performance computing will allow the student to maximize the potential of the lab's hardware and data access.
- Sequencing: Hands-on experience processing and analyzing Shotgun Metagenomic, 16S rRNA sequencing, and Bulk-scale RNA sequencing data.
- Machine Learning: Prior experience building and optimizing deep learning models, specifically generative architectures (VAEs) or attention-based models (TabNet), for high-dimensional biological data.
- Predictive Modeling: Experience with Polygenic Risk Score (PRS) assessment and validation.
- HPC Experience: Experience managing large-scale workflows on High-Performance Computing (HPC) clusters.
About the Lab & Resources
The University of Arkansas provides a world-class infrastructure for computational research. Students in Dr. Goswami's lab will have the opportunity to run complex models on the Pinnacle Supercluster, which features dedicated GPU nodes for accelerated deep learning and genomic processing.
- Computing Power: Access to the Pinnacle Supercluster featuring dedicated V100 GPU nodes.
- Collaborative Environment: A culture that encourages bridging theoretical data science with real-world biological applications.
How to Apply
The review of applications is conducted on a rolling basis, and interested candidates are encouraged to submit their materials as soon as possible to ensure full consideration for the Fall 2026 intake.
To apply, email the following to Dr. Aranyak Goswami at garanyak@uark.edu:
- Updated CV and Transcripts.
- Standardized Test Scores (GRE/SAT).
- Statement of Research Interests and contact details for three references.
Subject Line: Fall 2026 PhD Application - Animal Sciences
