Team Leader
Selim Romero
Texas A&M University
Department of Nutrition | Department of Veterinary Integrative Biosciences | CPRIT Single Cell Data Science
ssromerogon@tamu.edu
Project Type
Research
Who Can Join
Graduate Students, Masters Students, Undergraduate Students
Project Description
The Cai Lab at TAMU operates at the intersection of human genetics, computational statistics, and data science. Our current research focuses on deciphering the intricate behaviors of cells using advanced machine learning, network theory, and quantum computation. We develop novel analytical frameworks to process and interpret single-cell omics data, providing unprecedented insights into cellular function and regulatory mechanisms. A core specialization is the development of algorithms to decrypt complex biological phenomena, particularly in identifying and understanding gene signatures associated with specific conditions. We apply our expertise to study the genetic basis of phenotypic variability in the human population and develop robust computational tools to identify genetic variants underlying susceptibility to various genetic disorders. Our work aims to advance fundamental biological understanding and contribute to the development of precision medicine approaches.
Team Needs
We are seeking highly driven and intellectually curious team members who can significantly complement our lab’s advanced research. Ideal candidates will possess a robust foundation or strong aptitude in computational skills, logical reasoning, and independent self-learning.
While an interest in computational biology, data science, quantum computing, or genetics is crucial, we particularly value individuals demonstrating:
o Exceptional Computational & Logical Proficiency: A strong grasp of computational concepts and the ability to apply rigorous logical thinking to complex problems. Experience or a keen desire to learn Python, MATLAB, machine learning frameworks, or statistical programming is highly beneficial.
o Strong Scientific Comprehension & Analytical Reading: The capacity to efficiently read, critically evaluate, and thoroughly comprehend scientific literature and existing research, integrating new knowledge effectively.
o Excellent Written Communication Skills: The ability to articulate complex ideas clearly and formally in written reports and scientific documentation.
o Proactive Self-Learning & Adaptability: A proven track record or clear passion for independently acquiring new skills and knowledge, especially in rapidly evolving fields like quantum computation, single-cell omics, or advanced algorithms.
o Results-Oriented Drive: A motivated and formal attitude towards accomplishing tasks efficiently, meeting deadlines, and maintaining high standards of quality.
Prior exposure to single-cell RNA sequencing data or graph theory is a plus, but less critical than the foundational computational, logical, and self-learning capabilities we seek. We are committed to mentoring individuals who are eager to tackle cutting-edge biological problems and contribute significantly to our interdisciplinary research environment.
Special Opportunities
:Joining the Cai Lab’s Aggie Research Co-ops team offers a unique opportunity to engage in cutting-edge, interdisciplinary research at the forefront of human genetics and computational biology. Team members will gain hands-on experience with real-world single-cell omics datasets and learn to apply advanced machine learning, quantum computing, and network theory techniques to solve significant biological problems. This project provides an excellent platform for developing stronger computational, analytical, and problem-solving skills. Participants will also have the chance to contribute to the development of novel algorithms and potentially co-author publications. We foster a supportive and collaborative environment where team members receive mentorship and gain valuable insights into academic research, preparing them for future careers in academia or industry.