Current Opportunities

The Johns Hopkins University’s Department of Computer Science invites applications for tenure-track faculty positions at all levels and across all areas of computer science. We are particularly interested in applicants in computer vision, networked systems, theoretical computer science, and machine learning. The search will concentrate on candidates applying at the Assistant and Associate Professor levels. However, all qualified applicants will be considered.

The Department of Computer Science has 32 full-time tenured and tenure-track faculty members, 7 research and 8 teaching faculty members, 225 PhD students, over 200 MSE/MSSI students, and over 700 undergraduate students. There are several affiliated research centers and institutes including the Center for Computational Biology (CCB), the Laboratory for Computational Sensing and Robotics (LCSR), the Center for Language and Speech Processing (CLSP), the JHU Information Security Institute (JHU ISI), the Institute for Data Intensive Engineering and Science (IDIES), the Malone Center for Engineering in Healthcare (MCEH), the Institute for Assured Autonomy (IAA), and the Mathematical Institute for Data Science (MINDS). More information about the Department of Computer Science can be found at and about the Whiting School of Engineering at

The department is conducting a broad and inclusive search and is committed to identifying candidates who through their research, teaching and service will contribute to the diversity and excellence of the academic community. More information on diversity and inclusion in the department is available at

Applicants should submit a curriculum vitae, a research statement, a teaching statement, three recent publications, and complete contact information for at least three references.

Applications must be made on-line at While candidates who complete their applications by January 6, 2023 will receive full consideration, the department will consider applications submitted after that date. Questions may be directed to

The Johns Hopkins University is committed to equal opportunity for its faculty, staff, and students. To that end, the university does not discriminate on the basis of sex, gender, marital status, pregnancy, race, color, ethnicity, national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status or other legally protected characteristics. The university is committed to providing qualified individuals access to all academic and employment programs, benefits and activities on the basis of demonstrated ability, performance and merit without regard to personal factors that are irrelevant to the program involved.

If you love developing fundamental machine learning models and algorithms inspired by complex phenomena such as natural language, please consider joining Johns Hopkins University as an Assistant Research Professor of Computer Science. You would be affiliated with the Center for Language and Speech Processing (CLSP), one of the largest and most influential academic groups in NLP and speech.

This is a great launching pad to start an academic career, or to move back into academia from industry. This opportunity is appropriate for individuals who are also considering postdoctoral, industrial, or tenure-track employment. It provides a competitive salary and an excellent platform for career advancement. Initially, you would be working closely with Jason Eisner and his excellent students, helping to advise and develop a range of ongoing research projects.

Appointment renewal will be based on performance and funding availability. Currently funding is available for the first 3 years. There would be several paths for you to raise further funding if you wished to continue in the position. The university has instituted a non-tenure track career path for full-time research faculty culminating in the rank of (full) Research Professor.

Desirable qualifications for this position:

  • D. in computer science or a closely related field
  • Background in ML, preferably structured prediction and/or reinforcement learning
  • Background in NLP, preferably with knowledge of linguistics or grammar formalisms
  • Some familiarity with programming language design, data structures / algorithms, or HCI
  • Strong writing skills, including mathematical exposition
  • Intellectual curiosity, scientific integrity, concern for experimental design
  • Management and mentoring skills

Application Instructions:
Applications may be submitted online through Interfolio by clicking here. Questions should be directed to . Start date of summer or fall 2021 (negotiable). Applications will be reviewed until the position is filled.

The Johns Hopkins Individualized Health Initiative (Hopkins inHealth) is a University-wide, collaborative venture to bring advances in statistical science and machine learning to healthcare. Our mission is to: discover new ways to more precisely define, measure, and communicate each person’s unique health state and the trajectory along which it is changing; develop these discoveries into new methods that can be used to better inform patients and their clinicians, resulting in better medical care decisions and improved health outcomes; and apply new knowledge gained from the delivery of individualized care to produce better health outcomes at more affordable costs for whole populations.

As part of this unique initiative, we are seeking applicants for multiple 2-year postdoctoral and research scientist positions. Researchers will have the opportunity to gain broad exposure to topics in statistical science and machine learning and their applications to healthcare through regular interactions with other faculty and fellows within inHealth, and across their home departments of biostatistics and computer science. Both Johns Hopkins and All Children’s Hospital provide highly supportive and dynamic environments for junior investigators to grow and develop their future career.

Example projects include:

  1. More than 80 known types of autoimmune disorder afflict up to 50 million Americans, an estimated 5-8% of the population. A significant challenge in treating individuals with these conditions is that presentation of the disease varies greatly across individuals. By using electronic health data captured over decades from tracking individuals with the disease, the goals for this project are to develop methods to enable caregivers to tailor treatment options to the individual.
    Primary investigators: Suchi Saria, Assistant Professor of Computer Science; Antony Rosen, Professor of Medicine and Rheumatology; Michelle Petri, Professor of Medicine and Rheumatology; Scott Zeger, Professor of Biostatistics
  2. Can one reliably infer changes in health status using symptom data captured using sensors embedded within phones? The goals of this project are to understand how smartphones can be used in everyday settings to monitor health in individuals with neurodegenerative disorders. This project will involve developing novel methods for measuring an individual’s health status over time and methods for individualizing interventions.
    Primary investigators: Suchi Saria, Assistant Professor of Computer Science; Ray Dorsey, Professor of Neurology University of Rochester
  3. Can we use large scale population databases to measure effects of interventions on individuals? The goal of this project will be to develop novel statistical methods for individualizing diagnosis and treatment decisions and for evaluating the causal effects of interventions on children’s health outcomes.
    Primary investigators: Elizabeth (Betsy) Ogburn, Assistant Professor of Biostatistics; Scott Zeger, Professor of Biostatistics; Jonathan Ellen, MD, Professor of Pediatrics and Epidemiology Johns Hopkins University and President of All Children’s Hospital Johns Hopkins Medicine (ACH)

Applications are also welcomed from applicants interested in exploring other areas of methodological research in the intersection of machine learning, Bayesian analysis, causal inference, and computational health.

Bios for inHealth Methods Investigators:

Elizabeth (Betsy) Ogburn is an Assistant Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. She received her PhD in biostatistics from Harvard University, where she worked with Andrea Rotnitzky and Jamie Robins, followed by a postdoctoral fellowship with Tyler VanderWeele at the Harvard School of Public Health Program on Causal Inference. She works on developing statistical methodology for causal inference, with a focus on novel data sources and structures—for example, using electronic medical records to inform individual-level healthcare decisions and using social network and other data that evince complex dependence among observations. She collaborates with medical professionals, mathematicians, political scientists, and researchers across public health, and her research has received special recognition from a number of organizations, including the Journal of the Royal Statistical Society and the Atlantic Causal Inference Conference.

Suchi Saria is an Assistant Professor in Computer Science, with a joint appointment in the Institute of Computational Medicine at Johns Hopkins University. Her research focuses on developing machine learning and statistical inference methods for modeling temporal systems, especially in healthcare. In her work, she developed one of the first studies modeling health trajectories in infants from routinely collected electronic health data; this led to a novel non-invasive and accurate risk stratification score for measuring health at birth in preterm infants, a technology now licensed by one of the largest monitoring companies in Japan. Her works have received recognition in the form of best paper nominations at the Uncertainty in AI and the American Medical Informatics Association meetings, a cover article in Science Translational Medicine, a Gordon and Betty Foundation award, a Google Faculty research award, and a National Science Foundation Computing Innovation fellowship. She did her PhD with Daphne Koller from Stanford University, and her postdoctoral training with Ken Mandl and Zak Kohane at Harvard University.

Scott Zeger is a Professor of Biostatistics, and the Director of the Johns Hopkins Individualized Health Initiative. With his colleague Kung-Yee Liang, Dr. Zeger discovered the generalized estimating equation (GEE) approach to regression analysis for correlated responses as occur in longitudinal, time series, genetic and other studies. This work made Dr. Zeger one of the 10 most cited mathematical scientists over parts of the last two decades. With colleagues Diggle, Heagerty, and Liang, Zeger has written ’The analysis of Longitudinal Data’ published by the Oxford University Press.

Why Johns Hopkins?

For more than a century, Johns Hopkins has been recognized as a leader in medical research and teaching, with a history of successfully combining innovation at the forefront of engineering and medicine. You will have access to:

  • The Johns Hopkins health system includes six academic and community hospitals, four suburban health care and surgery centers, more than 30 primary health care outpatient sites, as well as programs for national and international patient activities. The Johns Hopkins Hospital is the only hospital in history to have earned the number one ranking by U.S. News for 22 years—an unprecedented 21 years in a row from 1991 to 2011, and again in 2013.
  • The Bloomberg School of Public Health at Johns Hopkins, specializing in research on health and wellness nationally and internationally, has consistently earned the number one rank by U.S. News since 1994, which was the first year the magazine began ranking schools of public health.
  • Hopkins has many top ranked programs and institutes at the intersection of engineering, medicine and data science, including departments of Biomedical Engineering, Biostatistics, the Institute of Computational Medicine, the Institute for Data Intensive Science and Engineering, and the Laboratory for Computational Sensing and Robotics. These groups host regular seminars and eminent visitors that provide broader exposure on the above mentioned topics.

Qualifications: The ideal applicant should have

  1. A PhD degree and publication record in a statistical science, machine learning or other data analysis field.
  2. Strong programming skills in a statistical language (R, MATLAB, SAS).
  3. Creativity, enthusiasm, and good communication skills.
  4. Interest in working on health problems, but prior experience not required.

How to Apply: Interested applicants should submit their curriculum vitae, selected paper(s), 2 references, and a brief cover letter summarizing background and interest to Suchi Saria. Applications will be considered until the position is filled. The Johns Hopkins University is an Affirmative Action / Equal Opportunity Employer. There are no citizenship restrictions for this position.