Post-Doctoral Fellowship - FELIX Laboratory

Post-Doctoral Fellowship - FELIX 2.0 Laboratory

Requisition #: A-180360-11

Status:
Full Time

Type:
Postdoctoral Fellowship

School:
School of Medicine

The Johns Hopkins University School of Medicine Department of Radiology in the Russell H. Morgan Department of Radiology and Radiological Science in Baltimore, MD is seeking a post-doc research fellow to be hired in the FELIX 2.0 laboratory. ( (Use the "Apply for this Job" box below). ). This is a full-time, post-doc position.

We offer a competitive salary, vacation and excellent benefits.

The FELIX 2.0 laboratory aims to change the trajectory of how pancreatic cancer is detected, how patients are evaluated, and improve outcomes. Its mission is to harness the power of artificial intelligence, help radiologists and clinicians in detecting pancreatic cancer when it could have otherwise been missed. This is an innovative way of approaching what has been a challenge for many decades.

Between Johns Hopkins Medicine, the Lustgarten Foundation, and Microsoft AI for Good, the FELIX 2.0 laboratory is pooling vast resources to improve how we do things, especially considering recent advancements in AI technology.

The position will include developing radiomics and deep learning models from contrast-enhanced computed tomography images to characterize pancreatic tumors, including cancer, cysts and other tumors. Different machine-learning approaches will be compared, and models validated on data prospectively collected. The position will include working with internal and external databases and help with development of our research into pancreatic cancer and other pancreatic tumors.

The candidate will contribute to coordinating expert interpretation and manuscript writing under supervision.

• Collect and organize information and data that supports study.
• Apply robust pipelines for radiomics characterization of CT images.
• Develop and implement machine learning and deep learning algorithms to analyze medical images.
• Perform statistical analysis to compare performance of the different approaches.
• Prepare reports and study finding to present to PI.
• Prepare research manuscripts.

• Proficiency in programming language such as Python, C++, R and MATLAB
• Strong theoretical understanding and practical experience in deep learning-based machine learning or natural language processing
• Strong background in statistical modeling
• Scientific writing
• Expertise in medical imaging processing or previous work in collaboration with healthcare professionals will be a plus.

- Ph.D. in computer science, biomedical engineering, or a related field.

- One year of experience with machine leaning, deep learning and data analysis

The referenced salary range represents the minimum and maximum salaries for this position and is based on Johns Hopkins University's good faith belief at the time of posting. Not all candidates will be eligible for the upper end of the salary range. The actual compensation offered to the selected candidate may vary and will ultimately depend on multiple factors, which may include the successful candidate's geographic location, skills, work experience, internal equity, market conditions, education/training and other factors, as reasonably determined by the University.

Total Rewards

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Equal Opportunity Employer

The Johns Hopkins University is committed to equal opportunity for its faculty, staff, and students. To that end, the university does not discriminate on the basis of sex, gender, marital status, pregnancy, race, color, ethnicity, national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status or other legally protected characteristic. The university is committed to providing qualified individuals access to all academic and employment programs, benefits and activities on the basis of demonstrated ability, performance and merit without regard to personal factors that are irrelevant to the program involved.

Pre-Employment Information

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The successful candidate(s) for this position will be subject to a pre-employment background check including education verification.

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