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The post-doctoral research fellow will execute research projects under the supervision of a principal investigator, with a focus on developing skills to transition the trainee from mentored research to independent research. Research topics include:

-Development and implementation of ontological systems for cancer care

-Knowledge representation approaches

-Innovative methodologies to facilitate findability, accessibility, interoperability, and reusability (FAIR) of clinical information from diverse sources (i.e., electronic health records, lab data sources, radiotherapy treatment planning systems)

-Integration of multimodal data (i.e., imaging, genetic, socioeconomic data) to identify high-risk cancer populations or health disparities

-Development of biomedical and computational methods and approaches (i.e., machine learning/artificial intelligence) to aid in the early diagnosis and management of acute and late cancer therapy-related toxicities

-Development and validation of reinforcement learning approaches for algorithmic management of toxicities such as acute pain during and after radiotherapy (i.e., to minimize opioid usage/dependence)

-Development of novel and robust data analysis algorithms to tackle causal mechanisms of action for the onset and progression of head and neck cancer disease

-ML/AI-based optimization of clinical procedures for precision or dental care

-Computational modeling for treatment planning and assessment of head & neck cancer treatment outcomes

The post-doctoral fellow will also assist the principal investigator with mentoring junior lab members and leading collaborative projects between the research team, clinical staff, and rotating researchers.

LEARNING OBJECTIVES

1. Provide analysis and develop algorithms and software for clinical and scientific problems.

2. Projects may include all of the above found in the summary, including generation of dosimetric analyses, machine learning with or without integration of ontologies, toxicity assessment, ontology development, clinical data review, and/or dental science components.

3. Gain expertise in clinical informatics with a focus on advanced or semi-automated methods for making data ML/AI-ready and ontological systems.

4. Training and supervising other research personnel on techniques and equipment procedures.

5. Standardization of database records and nomenclature to optimize semantic interoperability.

6. Collect, analyze, and plot data using appropriate software applications, and store and record data on computer and lab notebooks.

7. Assist in writing of scientific papers for publication, grant applications, and presentations at scientific meetings.

8. Other duties as assigned.

ELIGIBILITY REQUIREMENTS

Required Education: PhD in Computer Engineering, Applied Mathematics, Computer Science, Statistics, Computational Biology, or related field.

Required Experience: Three years of experience in data/computer science applications including ML/AI modeling, data curation, and ETL pipeline development or data architecture strategies. With a master's degree, one year of experience is required. With a PhD, no experience is required.

Preferred: Experience with reinforcement learning approaches. Experience handling clinical and/or multidimensional data. Experience with electronic health record automatic data extraction, transformation, database design, and biomedical informatics is highly preferred. Experience with natural-language processing is also preferred.

POSITION INFORMATION

MD Anderson follows the NIH stipend levels as outlined by the Kirchstein - NRSA (https://grants.nih.gov/grants/guide/notice-files/NOT-OD-23-076.html) . This full-time trainee position will provide a salary between $56,484 to $68,604, dependent upon the years of postgraduate experience.

MD Anderson offers compensated trainees:

• Paid medical benefits (zero premium) starting on first day for trainees who work 30 or more hours per week

• Group Dental, Vision, Life, AD&D and Disability coverage

• Paid Education Vacation and Sick Leave

• Paid institutional holidays, wellness leave, childcare leave and other paid leave programs

• Teachers Retirement System defined-benefit pension plan and two voluntary retirement plans

• Employer paid life, AD&D and an illness-related reduced salary pay program

• Health Savings Account and Dependent Care Reimbursement flexible spending accounts

• Fertility benefits

• State of Texas longevity pay

• Extensive wellness, fitness, employee health programs and employee resource groups

FACULTY MENTOR

Amy Moreno, MD