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A Research Intern Position is available in the department of Cardiology Research under the mentorship of Dr. Cezar Iliescu.

The research intern will be trained in machine learning algorithms and their application to large national datasets and local registries. The intern will gain hands-on experience in data preprocessing, model development, and analysis of large-scale health datasets to uncover insights in areas such as epidemiology, public health, or clinical outcomes. The program focuses on the practical application of machine learning methods, such as supervised and unsupervised learning, deep learning, and data integration techniques, to solve real-world research problems.

A key component of this role involves working with large, complex datasets, including national health databases and local health registries, to explore patterns and trends that can inform healthcare decision-making. The research intern will collaborate with researchers and data scientists to develop, test, and refine machine learning models, contributing to impactful research initiatives.

All duties and responsibilities are carried out in compliance with institutional policies, ethical research standards, and applicable federal and state regulations.

*LEARNING OBJECTIVES*

• Data Management and Preprocessing: Assist in the collection, cleaning, and preprocessing of large national datasets and local health registries. Ensure data is properly formatted for analysis and is free of errors.

• Machine Learning Model Development: Participate in the design and implementation of machine learning models (including supervised/unsupervised learning, deep learning, and data integration models) for analyzing complex datasets.

• Data Analysis and Interpretation: Analyze large-scale health data to identify trends, patterns, and correlations. Use machine learning tools to derive actionable insights for research and decision-making.

• Collaboration and Research: Work closely with research teams, including data scientists, clinicians, and domain experts, to apply machine learning methods to real-world research problems and interpret findings.

• Documentation and Reporting: Maintain detailed records of data processing, model development, and results. Prepare reports and presentations to communicate findings to the research team and stakeholders.

*ELIGIBILITY REQUIREMENTS*

Completion of a master's degree (MS) or higher in a relevant scientific field, such as Biomedical Sciences, Molecular Biology, Bioinformatics and Computational Biology, Biotechnology, Systems Biology, Biochemistry, Pathology, or Data Science (with a focus on biological applications).

Strong interest in applying machine learning algorithms to large health datasets and registries for epidemiological, clinical, or public health research.

Proficiency in programming languages such as Python, R, or similar; familiarity with machine learning libraries including TensorFlow, Keras, Scikit-learn; and experience handling large datasets.

Previous coursework or research experience involving machine learning or data analysis is preferred but not required.

Eagerness to learn and apply new technologies and techniques in machine learning and data science.

Ability to work effectively in a multidisciplinary team environment, collaborating with clinicians, data scientists, and public health experts.

*POSITION INFORMATION*

Offsite work arrangements are subject to approval and may be modified or revoked at any time based on business needs, performance considerations, or regulatory requirements.

This position may be responsible for maintaining the security and integrity of critical infrastructure, as defined in Section 113.001(2) of the Texas Business and Commerce Code and therefore may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment.

It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state or local laws unless such distinction is required by law. [http://www.mdanderson.org/about-us/legal-and-policy/legal-statements/eeo-affirmative-action.html](http://www.mdanderson.org/about-us/legal-and-policy/legal-statements/eeo-affirmative-action.html)