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AbbVie Post-Doctoral Fellow in Deep Learning in Lake County, Illinois

Post-Doctoral Fellow in Deep Learning

USA, Illinois, Lake County

Research & Development

Requisition # 1906145

At AbbVie, Preclinical Safety (PCS) is responsible for all aspects of Toxicology, Pathology, and Comparative Medicine. We partner with Discovery teams to select and investigate new targets and lead molecules, and work with project teams to develop and execute non-clinical safety development plans that support projects from pre-IND through registration and post-marketing phases. The Department of Pathology in PCS at AbbVie has an opening for postdoctoral fellow position to develop deep learning methods for toxicologic pathology. This postdoctoral position will be based at Lake County, Illinois

Key responsibilities:

The application of deep learning (DL)/machine learning (ML) to histopathology is a rapidly expanding field of bioinformatics. Its application to pathology is currently largely restricted to oncology. This position will be focused on developing reproducible deep learning methods to non-clinical toxicity studies with the objective to improve efficiency in evaluating histopathology data and develop tools for better characterization of toxicity.

  • Working with a team of pathologists to design and apply “fit for purpose” algorithms to histopathology data, primarily H&E stained slides.

  • Apply these algorithms to large training data and test sets to determine their accuracy and reproducibility.

  • Develop DL/ML tools for tissue segmentation and morphologic endpoint quantification.

  • Passion for solving technical problems and applying new technologies to further scientific goals.

  • Present and report data for internal and external meetings.

  • Expected to be member of the scientific community by publishing in top-tier conferences and journals, and collaborate with peer researchers


  • Successful completion and defense of a Ph.D in a background in bioinformatics, artificial intelligence, computer science, pathology informatics, or biomedical engineering

  • Recent graduate from accredited and nationally ranked university

  • Demonstrated skills in computer vision, machine learning/deep learning

  • Proficient in one or more languages and platforms: Java, Python, C++, C, JavaScript, MATLAB, R, GoogleNet, Caffe.

  • Knowledge in using AI platforms, technologies, and techniques preferred (e.g. Tensor Flow, Apache MXnet, Theano, Keras, CNTK, scikit-learn, H2O, Spark MLlib, etc).

  • Familiarity with digital pathology, conventional image analysis software (HALO/Visiopharm/Definiens), and anatomic pathology is desired

Application requirements:

  • Curriculum Vitae

  • A narrative on why your academic training qualifies you for this position and why you have an interest in this position.

  • Three letters of recommendation, preferably one from your PI and at least one from a member of your thesis committee.

  • Must be authorized to work in the U.S.

Key AbbVie Competencies:

  • Builds strong relationships within and outside a team to enable higher performance.

  • Learns fast and grasps the “essence” of a result and can change course quickly when indicated.

  • Raises the bar and is never satisfied with the status quo.

  • Creates a learning environment, open to suggestions and experimentation for improvement.

  • Embraces the ideas of others, nurtures innovation and manages innovation to reality.

Posting Grade: 15.

Job Grades are determined by the country in which the payroll is based.

Additional Information

  • Significant Work Activities and Conditions: Continuous sitting for prolonged periods (more than 2 consecutive hours in an 8 hour day)

  • Travel: Yes, 10 % of the Time

  • Job Type: Graduate Job

  • Schedule: Full-time

Equal Employment Opportunity Employer

At AbbVie, we value bringing together individuals from diverse backgrounds to develop new and innovative solutions for patients. As an equal opportunity employer we do not discriminate on the basis of race, color, religion, national origin, age, sex (including pregnancy), physical or mental disability, medical condition, genetic information gender identity or expression, sexual orientation, marital status, protected veteran status, or any other legally protected characteristic.