Employment Opportunities

The Johns Hopkins University’s Department of Computer Science invites applications for tenure-track faculty positions. We anticipate making multiple offers across all areas of the department and at all ranks. We offer an early action application option and support spousal/partner placement.

Early Action. Full consideration will be given to candidates who submit applications by December 1, 2024. However, beginning October 1, 2024, the department may take early action to schedule fall semester interviews and will consider fall offers with typical spring deadlines. We encourage candidates to apply early to take advantage of flexible scheduling and potentially receive an early offer before they proceed to spring interviews. All applications submitted by December 1, 2024, will receive full consideration.

Our search includes two tracks: 1) data science and AI and 2) all other areas of computer science. The data science and AI track encompasses all related areas (e.g., natural language processing, computer vision, robotics, etc.) and support for the following cross-departmental clusters:

• Foundational methods of machine learning, data science, and AI
• Embodied AI systems
• Health and medicine
• Scientific discovery
• Engineered AI systems
• People, policy, governance, and ethics of AI
• Security and safety of autonomous systems

Our search supports the large-scale expansion of the Whiting School of Engineering, which will add 150 new tenure-track professors at all ranks, including 30 Bloomberg Distinguished Professorships and 80 positions that will be part of the university’s new Data Science and AI Institute. This expansion includes a new building and extensive computational resources that will establish Johns Hopkins as one of the largest and leading engineering schools with a top AI research program. The expansion will grow JHU CS to become one of the largest computer science departments at a U.S. private university.

The department currently has 38 full-time tenure-track faculty members, 7 research and 8 teaching faculty members, 225 PhD students, over 200 master’s students, and over 700 undergraduate students. We are affiliated with several research centers and institutes including the Center for Computational Biology, the Laboratory for Computational Sensing and Robotics, the Center for Language and Speech Processing, the Information Security Institute, the Institute for Data Intensive Engineering and Science, the Malone Center for Engineering in Healthcare, the Institute for Assured Autonomy, the Mathematical Institute for Data Science, and the SNF Agora Institute. More information about the Department of Computer Science can be found here; more information about the Whiting School of Engineering can be found here.

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. We welcome candidates who are poised to address grand challenges in computer science, can work across disciplines to solve societal challenges, and support JHU’s leading role in increasing undergraduate diversity across elite universities. More information on diversity and inclusion in the department is available here.

The expected salary range for this position is $180,000–$500,000. The referenced salary range is based on the Johns Hopkins University’s good faith belief at the time of posting; actual compensation offered to the selected candidate may vary and will be based on factors including, but not limited to, the experience and qualifications of the selected candidate (e.g., years in rank, training, field, discipline, other work experience, and other similar factors), geographic location, internal equity, external market conditions, and other factors as reasonably determined by the university.

We offer dual career programs that support spousal/partner placement within the department, university, and the broader Baltimore/Washington area.

Applicants should submit a curriculum vitae, a research statement, a teaching statement, and (optionally) three recent publications. Junior (assistant) candidates should submit three to five letters of reference. Senior (associate/full) candidates should submit a list of references.

Applications must be made online here. While candidates who complete their applications by December 1, 2024 will receive full consideration, the department may consider applications submitted after that date. Furthermore, the department may take early action on applications beginning October 1.

Questions may be directed to fsearch2024@cs.jhu.edu.

The Johns Hopkins University is committed to equal opportunity for its faculty, staff, and students. To this 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.

Gillian Hadfield is seeking one or more highly qualified postdoctoral scholars to join her team at the Normativity Lab to investigate the foundations of human normativity and how these insights can inform the development of AI systems aligned with human values. The ideal candidate will have a background in interdisciplinary research and experience integrating social science concepts with computational modeling to explore the dynamics of AI systems and the development of autonomous AI agents. This is a full-time, one-year position, with the possibility of an extension.

About the Normativity Lab

How can we ensure AI systems and agents align with human values and norms? Maintain and enhance the complex cooperative economic, political, and social systems humans have built? What will it take to ensure that the AI transformation puts us on the path to improved human well-being and flourishing—and not catastrophe? Existing approaches to alignment— such as reinforcement learning from human feedback, constitutional AI, and social choice methods—focus on eliciting human preferences, aggregating them across multiple, pluralistic values if necessary, and fine-tuning models to satisfy those preferences. In the Normativity Lab, we believe these approaches are likely to prove too limited to address the alignment challenge and that alignment questions will require studying the foundations of human normativity and human normative systems.  We use economic, political, and cultural evolutionary theory together with computational modeling—specifically multi-agent reinforcement learning and generative agent simulations—to explore the dynamics of normative systems and to explore how to build AI systems and agents that have the normative infrastructure and normative competence to do as humans have learned to do, creating stable, rule-based groups that can adapt to change while ensuring group well-being. 

Specific Duties and Responsibilities:

• Project Ownership: Take ownership of research projects, working independently and collaboratively with a diverse team of experts.
• Model Development: Develop and refine computational models to simulate and analyze normative behaviors in various contexts.
• Data Collection and Analysis: Design and implement empirical studies, including data collection, statistical analysis, and interpretation of results.
• Publication: Prepare and submit manuscripts for publication in high-impact academic journals and present findings at conferences and workshops.
• Collaborate with Team Members: Work closely with lab members and external collaborators to foster a productive research environment and contribute to interdisciplinary projects.
• Mentor Students: Provide guidance and mentorship to graduate and undergraduate students involved in related research projects.
• Contribute to Lab Activities: Participate in lab meetings, potentially take a leadership role in coordinating lab activities, contribute to grant writing, and engage in outreach activities to promote the lab’s research initiatives.

Special Knowledge, Skills, and Abilities:

• Strong technical understanding of issues in the fields of AI safety and governance. Familiarity with the ethical implications of AI technologies and their alignment with human values and societal norms.
• Expertise in large language models, multi-agent reinforcement, or economic modeling and game theory.
• Knowledge of theories related to human normativity, including welfare economics, political theory, moral philosophy, cultural evolutionary theory, social norms, or legal systems is a strong asset; an interest in learning about these domains is essential.
• Proficiency in designing and implementing computational models to simulate normative behaviors and group dynamics.
• Strong capability in both qualitative and quantitative research methods, including statistical analysis and survey design.
• Experience conducting interdisciplinary research, including integrating perspectives from economics, law, and social sciences to analyze complex systems.
• Knowledge of the academic publishing process.

Level of Independent Decision-Making:

• Able to independently lead and deliver on research projects.
• Capable of formulating and implementing research methodologies and procedures.
• Able to make independent decisions regarding data quality, analysis, and interpretation.
• Capable of independently managing timelines and deliverables.

Qualifications:

• Advanced Degree: A PhD in a relevant field such as computer science, economics, political science or cultural evolution.
• Programming Skills: Proficiency in programming languages relevant to computational modeling (e.g., Python, R, or similar).
• Data Analysis Tools: Experience with statistical software and data visualization tools (e.g., SPSS, Stata, Tableau).
• Familiarity with AI Technologies: Knowledge of AI frameworks and algorithms, particularly those related to decision-making and ethical AI.
• Academic Excellence: Strong publication record in peer-reviewed journals relevant to normative systems, AI ethics, or related fields.
• Research Experience: Demonstrated experience conducting independent research, including project development and execution.
• Teaching and Mentorship: Experience in teaching or mentoring undergraduate or graduate students in a research or academic setting.
• Collaboration and Communication: Excellent teamwork skills with the ability to present research findings clearly and effectively to diverse audiences.

Applicants with some but not all of these qualifications are encouraged to apply and diversity of background and research interests will be considered.

Application Instructions:

Please submit the following documents via Interfolio:

• CV;
• Research statement identifying prior research projects, current interests, and sample research questions, aligned with the goals of the Normativity Lab, to be explored during the fellowship;
• Unofficial university transcript;
• Writing sample; and
• Contact information for three (3) people who can speak to the applicant’s professional experience and potential as a postdoctoral researcher.

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 two-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 disease presentation varies greatly across individuals. By using electronic health data captured over decades from tracking individuals with these diseases, the goals for this project are to develop methods to enable caregivers to tailor treatment options to each individual.
   Primary investigators: Suchi Saria, John C. Malone Associate 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 via 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, John C. Malone Associate Professor of Computer Science; Ray Dorsey, Professor of Neurology University of Rochester
3. Can we use large-scale population databases to measure the 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, Associate 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

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

Bios of inHealth Methods Investigators:

Elizabeth “Betsy” Ogburn is an associate 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 a John C. Malone Associate Professor of Computer Science with a joint appointment in the Institute for 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 John C. Malone Professor of Biostatistics and the director of the Johns Hopkins Individualized Health Initiative. With his colleague Kung-Yee Liang, Zeger discovered the generalized estimating equation approach to regression analysis for correlated responses as they occur in longitudinal, time series, genetic, and other studies. This work made Zeger one of the ten 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 and the forefront of engineering and medicine. You will have access to:

• The Johns Hopkins health system, which includes six academic and community hospitals, four suburban health care and surgery centers, more than 30 primary health care outpatient sites, and 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; it 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.
• Many top ranked programs and institutes at the intersection of engineering, medicine, and data science, including the departments of Biomedical Engineering and Biostatistics, the Institute for 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 aforementioned 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), two references, and a brief cover letter summarizing their 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.