Careers @ Oncospace
What is the opportunity
We are seeking a postdoctoral candidate for an exciting opportunity to help us develop cloud-based, AI-powered, data-driven applications for radiation oncology. The successful applicant will apply their skills in Machine Learning, Statistics, Computer Science, and Biomedical Engineering, to develop solutions that make the radiotherapy process more effective and streamlined.
This role will involve using large multi-dimensional datasets of clinical, technical, and imaging information to create machine learning models that predict expected levels of radiation dose to critical organs during radiotherapy treatment. The predictions are used to drive an efficient radiotherapy treatment planning process, evaluate the quality of existing plans, and help ensure each patient receives a safe and effective course of therapy. Your creativity and curiosity will ensure your work is of maximum benefit to cancer patients and their caregivers.
Who we are and what we do
Many of the foundational technologies used in radiation oncology to address these changes (e.g., dose calculation, delivery optimization, advanced visualization, and information systems) have reached a state-of-the-art level of sophistication, maturity, and capability. Yet, an opportunity exists to better harness these technologies and the information they create to optimize outcomes, for the patient and the practice.
Oncospace’s mission is to optimize outcomes for radiation therapy patients, radiation oncology practices, and our community, through the active collection of shared expertise, responsible application of technologies, and fostering of collaborative relationships.
We are a Baltimore-based, experienced team of innovators including medical physicists, data scientists, software engineers and information security experts.
Built cloud first on Microsoft Azure, our SaaS delivered, AI-powered, data-driven solution enables clinicians to confidently predict achievable high quality treatment plans for their cancer patients, consistently promote best practice behavior, and seamlessly share their expertise.
What you will be a part of accomplishing
This opportunity is specifically related to a Small Business Innovation Research (SBIR) grant awarded toOncospace by the National Science Foundation (NSF). The project is to build and commercialize software for the Peer Review of radiotherapy treatment plans, in collaboration with our clinical partners. Peer Review is an important step in ensuring the quality and safety of radiotherapy. Working with our development team, the Fellow will research ways to refine and implement dosimetric predictions that will help clinicians ensure minimal risk of radiation-related side effects for each of their patients.
You will make key contributions to the development of the Oncospace Peer Review product while working with clinical experts from our collaborating sites. You’ll have the opportunity to discover new ways to ensure the quality and safety of treatment for cancer patients, and to be involved in translational R&D from discovery to clinical use. You’ll also gain experience with a dynamic small company as you work with the Oncospace development team to develop innovative applications for clinical use.
How this fellowship is funded
This American Society for Engineering Education (ASEE) Innovative Postdoctoral Entrepreneurial Research Fellowship (I-PERF) position is funded by a supplement to a Phase II NSF SBIR grant awarded to Oncospace, Inc.
Who the ideal candidate is
The candidate must meet the requirements of Innovative Postdoctoral Entrepreneurial Research Fellowship (I-PERF). An ideal candidate will have:
- A Ph.D. in Data Science, Statistics, Computer Science, BME, or other data analysis discipline
- [Radiation physics] or Medical imaging analysis background
- Demonstrable software development experience
- United States Citizenship, National, or Permanent Resident status in the United States
Up for the challenge
To see if you are a fit, contact firstname.lastname@example.org with your CV today.