Applied Research Intern Jobs (NOW HIRING)

The primary purpose of the Research Intern position is to perform technical assistance and coordination skills in carrying out various research projects in the department of GI Medical Oncology. Lab is seeking a motivated individual who has interest and passion in science and is excited to be the forefront of the cutting-edge translational cancer research. The individual will record and evaluates data obtained in accordance with methods requested.

The lab is committed to the mentorship and growth of the new hires to ensure they can achieve their long-term career goals. All duties and responsibilities are carried out in compliance with institutional policies, ethical research standards, and applicable federal and state regulations. LEARNING OBJECTIVES Learning Objective 1: Develop Applied Bioinformatics and Multi-Omics Data Analysis Skills in Oncology Training and Research Activities: The trainee will receive hands-on training in bioinformatic analysis of cancer datasets, including next-generation sequencing, circulating tumor DNA (ctDNA), clinical outcomes, and molecular profiling data.

Under mentorship, the trainee will analyze patient-derived datasets generated at MD Anderson, develop data processing pipelines, and integrate molecular and clinical information to address translational oncology research questions. Expected Learning Outcomes: By the completion of the training period, the trainee will be able to independently perform bioinformatic analyses of cancer datasets, interpret molecular and clinical correlations, identify limitations and sources of bias in complex datasets, and communicate findings effectively to scientific audiences. Learning Objective 2: Apply Artificial Intelligence and Machine Learning Approaches to Precision Oncology Training and Research Activities: The trainee will leverage their prior experience in artificial intelligence and predictive modeling to develop and refine machine learning approaches for ctDNA-based biomarker discovery and patient outcome prediction.

The trainee will participate in model development, validation, performance evaluation, and interpretation using real-world clinical datasets. Expected Learning Outcomes: The trainee will gain proficiency in designing, implementing, and evaluating AI-driven predictive models for oncology applications. The trainee will develop expertise in model validation, performance assessment, and translating computational findings into clinically meaningful insights.

Learning Objective 3: Utilize Population-Level Clinical Data to Inform Cancer Risk Stratification and Patient Selection Training and Research Activities: The trainee will examine associations between geographic factors and KRAS-mutant colorectal cancer incidence, the trainee will analyze MD Anderson clinical datasets to investigate relationships between geographic, demographic, and molecular variables. These analyses will support efforts to improve patient triage and identify populations at increased risk for specific disease characteristics. Expected Learning Outcomes: The trainee will learn to integrate large-scale clinical, demographic, and molecular datasets to address population-level oncology questions.

The trainee will develop skills in epidemiologic analysis, data interpretation, and generation of clinically actionable hypotheses. Learning Objective 4: Strengthen Scientific Reasoning, Experimental Design, and Research Communication Training and Research Activities: The trainee will participate in regular project meetings, data reviews, manuscript preparation, and research presentations. The trainee will receive mentorship in study design, hypothesis generation, critical evaluation of results, and identification of future research directions.

Expected Learning Outcomes: The trainee will enhance their ability to formulate scientifically rigorous research questions, critically evaluate analytical approaches, articulate study limitations, and effectively communicate complex technical concepts to multidisciplinary audiences. The trainee will develop the skills necessary to independently contribute to translational cancer research projects. ELIGIBILITY REQUIREMENTS Education: Bachelor's degree with major course works in computer science.

Certification: None. Preferred Certification: None Experience: One year experience in processing large data sets and one year experience with AI modelling. Preferred Experience: None 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

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