Postdoctoral Research Fellow
The image processing and analysis core (iPAC) at the University of Massachusetts Medical School has an open position for a postdoctoral research fellow. There are a variety of projects to work on with a diverse team of experts in the areas of machine learning, deep learning, image processing, biomedical engineering, medical physics, radiology, and computer science. The postdoc’s job will include developing essentials algorithms and imaging pipelines for all image processing needs for multiple imaging modalities including MRI, CT, PET, SPECT, etc. The postdoc will be expected to analyze and process large imaging datasets, with a focus on handling and processing ‘Big Data,’ within the context of a medical setting.
- PhD in biomedical engineering, electrical engineering, MRI physics, data science, machine learning, statistics, computer sciences, mathematics, or related field.
- Strong programming skills in C/C++, Matlab, Python and relevant ML libraries such as scikit-learn, TensorFLow and PyTorch.
- Experience with advanced image analysis techniques including image registration techniques and machine learning approaches.
- Experience in working with medical imaging data such as MRI, CT, PET, etc.
- Experience with MRI data acquisition and image analysis is highly desirable especially with 3T clinical Philips and/or 7T preclinical Bruker systems.
- In-depth experience with modern and classical machine learning methods
- Strong statistical foundation with broad knowledge of supervised and unsupervised techniques
- Knowledge of a variety of machine learning techniques (clustering, classification, regression, neural networks, etc.) and their real-world advantages/drawbacks
- Proven ability to collaborate with others
- A passion for tackling challenging problems and developing creative solutions
- A drive for self-development with a focus on scientific know-how
- Communicate effectively with executive and clinical personnel
- Mine and analyze structured/unstructured data to create algorithms
- Identify information-rich features in large target datasets
- Formulate innovative solutions to problems where machine learning is applicable
- Collaborate with research faculty and medical personnel to produce the best possible AI product
- Recommend a scalable, reliable hardware/software/machine learning platform
- Maintain discussions of proposed features
- Tweak and debug in collaboration with peers
- Provide work effort estimates to management to assist in setting priorities
- Solve problems as they arise and communicate potential roadblocks to manage expectations
- Support existing research projects through the activities listed above