Postdoctoral Fellow - DSAI​/ROSEI

General Description

Dr. Nicolas Christianson is seeking a highly motivated postdoctoral researcher to join his research group at Johns Hopkins University beginning in Fall 2026. The position will be based in the Department of Computer Science, with opportunities to engage with the broader Johns Hopkins research ecosystem through the Data Science and AI Institute (DSAI) and the Ralph O'Connor Sustainable Energy Institute (ROSEI).

The postdoctoral researcher will work on problems in data-driven and machine learning-augmented algorithm design
, AI and machine learning for optimization
, and reliable, robust decision-making under uncertainty in complex systems
. The ideal candidate will be interested in developing rigorous algorithmic and methodological foundations and bringing these ideas to bear on impactful applications in sustainable computing infrastructure, data center workload and energy management, power and energy systems, and related domains. Applicants with strong mathematical, algorithmic, or machine learning backgrounds are encouraged to apply, even if their prior work has not focused on energy or computing systems.

This is a full-time, on-site postdoctoral position with an initial appointment of 12 months and the possibility of extension based on performance and funding. Strong candidates need not meet every qualification listed below.

• PhD in Computer Science, Electrical Engineering, Operations Research, Applied Mathematics, or a related technical field
• Strong publication record in relevant peer-reviewed venues, such as ACM SIGMETRICS, ACM e-Energy, NeurIPS, ICML, COLT, SODA, or related conferences and journals
• Strong technical background in one or more of the following:
• Algorithms, especially online algorithms, approximation algorithms, and learning-augmented algorithms
• Optimization, especially stochastic optimization, machine learning for optimization, and decision-focused learning
• Uncertainty quantification, including conformal prediction
• Machine learning theory, including online learning and bandits
• Interest or experience in working on topics related to computer systems, sustainable computing, data centers, power systems, smart grids, or electricity markets
• Proficiency in Python and machine learning frameworks (e.g., PyTorch)
• Experience with optimization tools such as Gurobi and CVXPY
• Experience building high-quality research code, running computational experiments, or contributing to open-source software.
• Ability to lead research projects independently, from problem formulation through analysis, implementation, and publication
• Excellent written and oral communication skills.
• Interest in working in a collaborative and interdisciplinary environment

The referenced salary range represents the minimum and maximum salaries for this position and is based on Johns Hopkins University's good faith belief at the time of posting. Not all candidates will be eligible for the upper end of the salary range. The actual compensation offered to the selected candidate may vary and will ultimately depend on multiple factors, which may include the successful candidate's geographic location, skills, work experience, internal equity, market conditions, education/training and other factors, as reasonably determined by the University.

Johns Hopkins offers a total rewards package that supports our employees' health, life, career and retirement. More information can be found here: (Use the "Apply for this Job" box below)..

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 characteristic. 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.