Senior Computational Scientist

University of Texas MD Anderson Cancer Center
Houston, Texas
Closing date
Oct 10, 2022

View more

Health Sciences
Organization Type
The mission of The University of Texas M. D. Anderson Cancer Center is to eliminate cancer in Texas, the nation, and the world through outstanding programs that integrate patient care, research and prevention, and through education for undergraduate and graduate students, trainees, professionals, employees and the public.

Quantitative imaging research is a key component to enable and guide personalized oncological patient care. The Tumor Measurement Initiative (TMI) aims to build an institutional platform to support standardized, automated, quantitative imaging-based tumor measurement across each patient's journey to advance multidisciplinary, data-driven, high precision cancer treatment.

This individual will be responsible for designing and managing the process of building automated image interpretation tools and the extraction of tumor measurements to fulfill the TMI objective. The activity is an important sub-component of the overall TMI activity and requires a combination of computational skill, subject matter (imaging) expertise, technical expertise and organizational diligence/competency.

The individual will be working with internal and external teams that are developing specific image analysis algorithms and will coordinate these developments in a fashion that supports scientific/technical evaluation and integration into the broader TMI effort. Scope includes regular collaboration and verbal updates with TMI and lab team members to assist on project/data intake, development, delivery, and ensure the alignment with the overall priority. Candidates must have an in-depth understanding of information systems with a specific focus on imaging principles and be able to communicate knowledge of the same to co-workers effectively. The individual must have experience with programming languages and scripting methods (Python, MATLAB, C++, R and/or SQL), machine learning/deep learning methods, data analysis, image analysis, and statistical analysis.

Candidates having experience with common open source scientific computing libraries such as OpenCV, PyTorch, and TensorFlow are preferred. Experience with database languages such as SQL is desirable.

Drive: Technical Expertise TMI Automation team support:
Designing and managing the process of building automated image interpretation tools and the extraction of tumor measurements to fulfill the TMI objective.
Participating in the TMI request intake process, which includes reviewing business requirements, design, specifications and solutions in accordance with institutional standards and requirements.
Conducting review of teams' efforts to ensure consistent methodologies are followed and make recommendations where necessary.
Identifying problems and related technical issues leading to longer term, broadly applicable solutions.

Tools/models development and management:
Developing solutions to ensure efficient processes for cohort generation required for training, validation, and interval quality assurance of TMI automation models/algorithms/tools.

Developing machine learning model management solutions and serving as the imaging technical subject matter expert.

Evaluating existing algorithms/tools and developing routines in an automated fashion that can be deployed to the broader TMI effort.

Collaborating with researchers and clinicians working on healthcare information systems (e.g., EPIC, PACS, RayStation, etc.) in the capture and curation of multidimensional data and developing computational modeling approaches for medical imaging.

Participating in the coordination of machine learning model management from development to clinical deployment.

Drive: Analytical Thinking Computational programming skills:
Developing/porting and optimizing scientific applications for high performance computing systems and applications at-scale.

Provide analysis of data, design and feasibility of proposed solutions.
Optimizing and extending algorithms, analysis pipelines, and software in order to establish and ensure effectiveness and scalability of computing infrastructure.

Demonstrating competency in the quantitative analysis of imaging data and work to develop deeper expertise on existing and emerging automated image analysis methods.

Coordination skills:
Participate in the coordination of platform governance (e.g. roles, security, access) of relevant systems and work with Data Governance & Provenance Office to ensure alignment of procedures for both platform and imaging data governance.

Translating scientific research needs into technical computing solutions and helping researchers conduct their project in a computationally effective way.

Professionalism: Oral and Written Communication Team support and guidance:
Liaising with IS Enterprise Development and Engineering technical teams to ensure supporting infrastructure and software needs are met.

Facilitating coordination between teams where there are dependencies or overlapping technical concerns to minimize duplication of effort.

Keeping current on relevant new technologies and understanding and communicating their impact on both research and clinical practice, and provide input on their place in the overall institutional imaging research strategy.

Other duties as assigned

Bachelor's degree with a concentration in Science, Engineering or related field.

Preferred: Master's degree or PhD with a concentration in Science, Engineering or related field.

Nine years of experience in scientific software programming with a concentration in scientific computing. With Master's degree, seven years experience required. With PhD, five years of experience required.

Preferred: Experience with common open source scientific computing/machine learning libraries (e.g. PyTorch / TensorFlow), container technologies (e.g. Docker) and container orchestration (e.g. Kubernetes) is preferred. Knowledge of version control protocols, static code analysis, unit testing and test-driven development, security testing and automated test frameworks is highly desired.

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.

Get job alerts

Create a job alert and receive personalized job recommendations straight to your inbox.

Create alert