Students who are interested in graduate education in my lab can apply to one of three PhD programs: 1. Electrical and Computer Engineering (ECE) at the McCormick School of Engineering 2. The Health & Biomedical Informatics track in the Health Sciences Integrated Program (HSIP) 3. The Computational Genomics track in the Driskill Graduate Program (DGP).

Students who succeed in our lab all have a passion for improving health or biomedical research, but have different levels of interest in application details and in prior experience with computing or machine learning. The ECE program is for students with an engineering, computer science, or mathematics background who have outstanding quantitative skills. This curriculum provides excellent training in methodology including machine learning, Bayesian estimation and probability theory, and high performance computing. The HSIP program is geared towards students who are interested in healthcare applications and who are familiar with computing, but who may not have engineering or computer science degrees. The HSIP curriculum covers biostatistics, healthcare environments and information systems, and includes elective opportunities in diverse areas like analytics and epidemiology. The DGP program is for students who have a background in biology and who are motivated to advance understanding of disease using computational approaches. These students are strongly motivated by biomedical applications and will receive graduate education in biology and computation.

Postdoctoral scholar - computational developmental / hematopathology

The Department of Pathology at Northwestern University is seeking a postdoctoral fellow to conduct research in computational pathology. This person will focus on developing machine learning tools to assist pathologists in their diagnostic work with emphasis on prospective application and operational integration of the developed tools. Researchers will have access to data and computational resources to develop, validate, and translate their tools including a growing institutional repository of whole-slide images, a research slide scanning facility that is integrated with our clinical lab, clinical and pathology data from the Northwestern Medicine Enterprise Data Warehouse, internal platforms for slide viewing, annotation, and data management, and a high-performance computing infrastructure dedicated to computational pathology. This is an excellent opportunity for a creative and enthusiastic individual with interests in medical applications that impact the way we diagnose and treat patients.

Our group is home to internationally recognized NIH funded investigators and was formed to synergize with clinical implementation of digital pathology at Northwestern. The expertise of our faculty spans engineering, informatics, and pathology. Our faculty conduct fundamental research in machine learning and artificial intelligence, participate with working groups to develop recommendations for clinical translation of computational pathology, work with disease-focused consortia to apply computational pathology in clinical and basic science research, develop public data resources for benchmarking and research, and create open-source platforms for that support the use of pathology imaging and computational tools in research.

This position will be jointly supervised by Dr. Lee Cooper and clinical faculty, with opportunities to collaborate with faculty in the Department of Pathology, the Lurie Cancer Center, and the McCormick School of Engineering. The Institute of Augmented Intelligence in Medicine will also present opportunities to interact with others conducting similar research in genomics, medical imaging, and clinical data analysis. The Department of Pathology is located on Northwestern’s Downtown Chicago campus in the Streeterville neighborhood.

Principle accountabilities:
‍-Develop methodology for analyzing images and related metadata
-Maintenance of software repositories and documentation of software
-Management of research datasets including images, clinical meta-data, annotations, computational results, and models
-Interact with collaborators, other postdocs, and graduate students to achieve project goals
-Development of manuscripts in collaborations with supervisor and collaborators
-Attendance at conferences and symposia where results will be presented in poster or talk formats
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Eligibility: PhD in engineering, computer science, or related fieldExperience in medical image analysis, computer vision, or deep learning. Expertise in Python programming and either TensorFlow or Pytorch. Knowledge of version-control tools and software engineering practices. Experience with Linux-based systems and command line tools.
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Preferred: Experience with parallel and distributed computing or execution of software on high performance computing systems.