The University of Texas MD Anderson Cancer Center has the potential to unlock the power of data by further developing and investing in talent, team science and infrastructure to optimize multidimensional data integration, analysis, and application for the benefit of patients with cancer. - - Ideal candidates will have experience developing deep learning models that can detect tumors on CT or MRI radiology images - - Dr. Caroline Chung leads an imaging computational laboratory within the Departme... more details
The University of Texas MD Anderson Cancer Center has the potential to unlock the power of data by further developing and investing in talent, team science and infrastructure to optimize multidimensional data integration, analysis, and application for the benefit of patients with cancer.
**Ideal candidates will have experience developing deep learning models that can detect tumors on CT or MRI radiology images**
Dr. Caroline Chung leads an imaging computational laboratory within the Department of Radiation Oncology at MD Anderson Cancer Center. The Chung lab's major research focus is to develop quantitative imaging pipelines and predictive tools to be used in: 1) tumor response assessment 2) treatment-related toxicity; and 3) personalization of radiotherapy and multimodal treatment. In addition, the lab is working on the standardization of collection and nomenclature of images to facilitate meaningful measurement and interpretation of imaging biomarkers across departments and institutions to support efforts aligned with the Institute for Data Science in Oncology.
Quantitative imaging research is a key component to enabling and guiding personalized oncological patient care. The Chung Lab has an additional role in supporting 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.
The primary purpose of the Assistant Data Scientist position is to provide support for building pipelines and tools to support data curation, analysis, extraction of tumor measurements and building predictive tools for tumor response and toxicity to normal tissues. This activity requires a combination of computational skills, technical expertise in quantitative imaging, understanding of oncology and oncology treatment and strong teaming and communication.
This individual will have demonstrated experience with programming languages and scripting methods (Python, MATLAB, C++, CUDA, Bash, and/or SQL), machine learning / deep learning methods, data analytics, and image analysis.
Successful candidates will collaborate with other data scientists, IT personnel, collaborating faculty and trainees to address key clinical challenges that impact our patients.
Drive: Technical Expertise Working with researchers to develop, adapt, and implement computational methods by applying deep learning methods and architectures for the datasets. Working with minimal oversight with researchers in analyzing, defining, and resolving analytical problems and bugs. Participating in discussion and implementation of machine learning model management solutions. With direction, develops and maintains algorithms/tools and infrastructure for resolving specific analytical problems. Working with faculty, IT personnel, and other researchers to respond to new technologies. Keeping abreast of continually evolving analytical tools and strategies. Maintaining high code quality and ensuring code is thoroughly and consistently tested before deploying for end user use. Organizing data and publishing code with documentation, in line with departmental standards. Providing support for existing software systems as they evolve.
Drive: Analytical Thinking Computational programming skills: Preparing and running QA testing on new features/components. Perform curation and analysis of data. Test and containerize publicly available, pre-developed containers and models to enrich the TMI container library. Working with end users to gather initial requirements. Assisting researchers to analyze a wide variety of clinical data, design, feasibility testing of proposed solutions, evaluate and interpret the results.
Professionalism: Oral and Written Communication Presenting results in collaboration meetings, and communicating with other team members to share information and tips. Communicating and assisting cooperatively and effectively with leaders, peers, end users and support teams when required.
Other duties as assigned
Required : Bachelor's degree in Biomedical Engineering, Electrical Engineering, Computer Engineering, Physics, Applied Mathematics, Science, Engineering, Computer Science, Computational Biology, or related field.
Preferred Education: Master’s Level Degree
.
Preferred Experience: Experience in scientific software development/analysis with common open-source scientific computing/machine learning libraries (e.g., PyTorch / TensorFlow), containerization, and cloud-native technologies (Docker & Kubernetes) is preferred.
Experience developing deep learning models that can detect tumors on CT or MRI radiology images
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
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