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Undergraduate Research Assistant

Howard University
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CANDIDATES MUST HAVE A FEDERAL WORK-STUDY AWARD FOR THE 2026-2027 SCHOOL YEAR.


The Howard University Department of Medicine and the Center for Applied Data Science and Analytics engage in cutting-edge research at the intersection of applied mathematics, computational biology, and physics. Our laboratory focuses on uncovering the underlying mechanisms of complex biological and dynamic systems through advanced computational modeling, multi-omics network integration, and single-cell analysis. Together with cross-institutional collaborators, our research aims to apply innovative frameworks, such as recurrent neural network like reservoir computing and nonlinear dynamics, to solve complex biological data challenges.

 

JOB PURPOSE:
The Research Assistant conducts computational research for the laboratory’s projects in nonlinear dynamics and reservoir computing. The Research Assistant thrives as both an independent self-starter and an engaged collaborator. The ideal candidate will share a commitment to scientific rigor and have a strong foundational interest in analytical problem-solving. The Research Assistant reports directly to the Principal Investigator.


JOB DUTIES:
 

  • Review and synthesize academic literature related to reservoir computing and dynamic network models.
     
  • Assist in setting up, running, and evaluating computational simulations.
     
  • Collaborate on data analysis, algorithmic troubleshooting, and statistical modeling.
     
  • Attend weekly meetings to discuss research progress and present findings from assigned academic papers.
     

QUALIFICATIONS:
Required:
 

  • Federal Work-Study Award for the 2026-2027 academic year.
     
  • Coursework in physics, mathematics, computer science, bioinformatics, or a related analytical field.
     
  • Excellent attention to detail and commitment to accuracy in scientific research.
     
  • Strong analytical and problem-solving skills.
     
  • Ability to meet project deadlines and shift priorities as needed.
     
  • Ability to work collaboratively with colleagues and supervisors.
     

Preferred:
 

  • Familiarity with programming languages used for scientific computing (e.g., Python, R, or MATLAB).
     
  • Experience or coursework related to differential equations, linear algebra, or machine learning.
     
  • Experience navigating academic databases (e.g., PubMed, IEEE Xplore, arXiv).
     
  • Familiarity with Linux/Ubuntu command-line environments.