Research Assistant

Howard University

3d•Onsite

About The Position

The Research Assistant will contribute to interdisciplinary projects within the Department of Internal Medicine at the Howard University College of Medicine, focusing on Artificial Intelligence (AI), Machine Learning (ML), Computational Medicine, and Data Analytics. This position supports ongoing research aimed at developing predictive, diagnostic, and intervention models that advance healthcare delivery, improve patient outcomes, and address complex challenges in precision and population health. The Research Assistant will work closely with the Principal Investigator and research collaborators to perform data-driven analysis, computational modeling, and literature reviews. This role provides valuable hands-on experience for students interested in research at the intersection of AI and medicine.

Requirements

Current enrollment in a Bachelor’s, Master’s, or Ph.D. degree program in Computer Science, Data Science, Biomedical Engineering, or a related field.
Must be at least a third-year undergraduate student (Junior or Senior status) or higher.
Demonstrated proficiency in Python and related libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, or PyTorch.
Familiarity with data analysis and visualization using tools such as Matplotlib, Seaborn, or Plotly.
Foundational understanding of AI, machine learning, and data science concepts.
Strong analytical thinking, problem-solving, and written communication skills.
Ability to work independently and collaboratively in a research environment.

Nice To Haves

Experience working with biomedical, clinical, or public health datasets.
Familiarity with cloud computing platforms (e.g., AWS, Google Cloud, or Microsoft Azure).
Experience with version control systems (e.g., Git/GitHub).
Prior research or academic publication experience.

Responsibilities

Assist with data collection, curation, preprocessing, and integration from diverse biomedical and clinical sources.
Conduct exploratory data analysis, data visualization, and feature engineering.
Develop, train, and evaluate machine learning and deep learning models.
Review scientific literature and summarize findings to support ongoing research projects.
Contribute to technical documentation, manuscripts, and progress reports.
Collaborate effectively with interdisciplinary teams across research and clinical domains.
Maintain accurate records of research workflows, results, and code repositories.