Postdoctoral Fellow - Radiation Oncology - Research

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 onset and progression of head and neck cancer disease,

- ML/AI based optimization of clinical procedures for precision orodental care, and

- 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.

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

* LEARNING OBJECTIVES
* 1.) Provides analysis and develops 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, plot data using appropriate software applications, 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
* Education Required:

Ph.D. or equivalent doctorate

Experience

Required:

Six years of experience in scientific or experimental research work

Preferred:
With preferred degree, four years of required experience

* POSITION INFORMATION
* MD Anderson offers full-time postdoc positions with a https://(Use the "Apply for this Job" box below). tuition benefits, educational opportunities, and individual and team recognition

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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