Image analysis algorithms can enable high throughput histologic scoring of well-defined tissue changes and routine animal models. We are looking to increase development of image analysis algorithms for automated histologic scoring of animal models with respect to utilizing image analysis tools to preserve pathologist time for novel/high value work and to shift routine work to rapid and cost effective solutions. We are requesting a position to work closely with scientists and pathologists to understand the model system to be evaluated, the questions being posed, and to determine which image parameters are most relevant to those questions. This is an interesting scientific opportunity for a candidate with interest in the computational aspects of understanding disease processes at the tissue level. The person will also be expected to validate algorithm results and determine a statistically acceptable degree of accuracy. The goal of the position would be to significantly contribute to the development of one or more validated algorithms which could be reproducibly applied to histologic scoring of research animal models.
Must have working experience or extensive course work related to biological image data analysis. Background or experience with Machine Learning or Deep Learning, particularly relating to image analysis and open-source image analysis packages, is preferred.
Other essential qualities include: basic programming skills (MATLAB, Python or R preferred), exceptionally collaborative personality, excellent written and oral communication skills, creativity, and adaptability to frequent changes.
Experience with microscopy is beneficial, but not necessary. Knowledge of histology, immunohistochemistry and neuroscience is preferable.
B.S. or advanced degree with adequate working experience in one of the following fields:
Computational biology, Bioinformatics, Digital signal and image processing, or Computer science.