Job Information
SUNY Upstate Medical University Postdoctoral Associate in Syracuse, New York
Job Summary:
The Pathology Research Core facility at the Department of Pathology is seeking highly motivated candidates for the postdoctoral research fellow positions with prior experience in translational cancer research projects using computational pathology, omics approaches and artificial intelligence. This opportunity involves working in the core lab under the mentorship of Dr Tamara Jamaspishvili, core Director and Assistant Professor of Pathology. The lab focuses on running large-scale, multi-disciplinary collaborative translational research projects using digital and computational pathology to develop artificial intelligence (AI)-based strategies to advance precision medicine and current pathology practice. Expertise and prior experience in biomarker research in translational/clinical oncology, particularly in the field of prostate cancer and immuno-oncology will be preferred. Publications, patents, or similar outputs demonstrating experience in the required domain will be highly valued.
The research topics and environment:
Developing innovative imaging biomarkers to better risk stratify cancer patients and creating AI-based algorithms for predicting treatment responses in cancer patients.
Developing and applying automated analytical pipelines for various types of pathology data, including histopathology images and multi-omics information.
Providing an opportunity to gain insights into academia-industry relationships or partnerships with biotech companies
Providing an experience in practice-driven implementation of digital pathology research projects.
Required skills and expertise:
Proficiency in data science and biostatistics for analyzing large-scale clinical and genomics data. Experience with numerical modeling, data visualization, and data analysis, utilizing statistical software tools such as R, SPSS, SAS, or others.
Experience with large-scale imaging data and formats (e.g., pathology images) and image analysis software (Visiopharm, Halo, QuPath, Image J, or others).
Experience with digital pathology and image analysis projects using open-source (QuPath, Image J, or others) or proprietary image analysis software (Visiopharm, Halo).
Experience analyzing different modalities, such as histopathology, radiology, and spatial transcriptomics, using state-of-the-art deep-learning techniques.
Experience in developing, training, and evaluating deep-learning models using public deep-learning frameworks (e.g., PyTorch, TensorFlow, Keras) is preferred.
Expertise in programming languages, such as Python, C++, Java, with familiarity with Linux is preferred. Familiarity with computer vision libraries and tools like OpenCV, scikit-image, and Dlib. Knowledge of GPU programming and optimization for accelerating deep learning computations.
Other Duties & Responsibilities
Review and synthesis of relevant literature.
Prepare manuscripts for publication in peer-reviewed journals.
Prepare grant proposals and assist the PI with grant submissions, renewals, and preliminary investigations to support research efforts.
Disseminate research at national and international conferences.
Guiding and monitoring a team of graduate and undergraduate trainees to build large-scale, well-annotated image datasets and research cohorts with comprehensive follow-up data.
Collaborate closely with pathologists, medical experts, and research teams to develop computer vision algorithms for analyzing and interpreting digital pathology images, including histopathology, gene expression, or spatial transcriptomics data.
Document research findings, methodologies, and codebase for internal knowledge sharing and external publications.
Effectively communicate and present research findings, project plans and AI strategies to both technical and non-technical team members and stakeholders.
This is a 1-year contract position, extendable up to 3 years based on performance. Candidates from diverse backgrounds are encouraged to apply. The position is open to national and international candidates
Minimum Qualifications:
Advanced degree (Ph.D., D.V.M., or M.D./Ph.D.) in life sciences and healthcare field, or computer science, or biomedical engineering.
Preferred Qualifications: Work Days:
Monday-Friday
Message to Applicants:
Recruitment Office: Human Resources