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Internship Computer Science Postdoc Jobs in Baltimore, MD

Umd

Post-Doctoral Associate

College Park, MD · On-site

$48K - $65K/yr

The postdoc may also organize and participate in relevant workshops to disseminate their work in the research community. MINIMUM QUALIFICATIONS: Education: Ph.D. in Computer Science or related fields.

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Internship Computer Science Postdoc information

What is the difference between Internship Computer Science Postdoc vs Research Scientist?

AspectInternship Computer Science PostdocResearch Scientist
Required credentialsTypically PhD or nearing completion in Computer SciencePhD in Computer Science or related field
Work environmentAcademic labs, universities, research institutionsResearch labs, industry R&D departments, universities
Employer and industry usagePrimarily academic and research institutionsIndustry companies, government agencies, academia
Common search and comparison intentUnderstanding early career research rolesExploring advanced research positions in industry or academia

Internship Computer Science Postdocs are typically short-term research positions for PhD candidates or recent graduates, mainly in academic settings. Research Scientists usually hold PhDs and work in industry or research labs, focusing on applied or fundamental research. While both roles involve research, internships are entry-level and temporary, whereas Research Scientists are more permanent and senior roles.

What cities near Baltimore, MD are hiring for Internship Computer Science Postdoc jobs? Cities near Baltimore, MD with the most Internship Computer Science Postdoc job openings:
Internship Computer Science Postdoc jobs near you
Infographic showing various Internship Computer Science Postdoc job openings in Baltimore, MD as of July 2026, with employment types broken down into 29% Internship, 53% Full Time, 6% Part Time, and 12% Temporary. Highlights an 100% In-person job distribution.
Postdoctoral Fellow - CausalML Lab

Postdoctoral Fellow - CausalML Lab

Johns Hopkins University

Baltimore, MD • On-site

$48K - $66K/yr

Full-time

Re-posted 9 days ago

Johns Hopkins Medicine rating

7.5

Company rating: 7.5 out of 10

Based on 205 frontline employees who took The Breakroom Quiz

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Job description

Description
Dr. Murat Kocaoglu is seeking highly motivated postdoctoral scholars to join CausalML lab at Johns Hopkins University in 2026. This position offers the opportunity to work directly with CausalML Lab (led by Dr. Kocaoglu), alongside its respective lab members.
The research focuses on developing fundamental algorithms for causal decision making using real-world, non-IID time series datasets. This work is primarily conducted at the Homewood Campus in Baltimore. The ideal candidate has experience with developing algorithms for non-IID or time-series data.
Applicants with strong credentials and motivation should still apply even if they lack these specific qualifications. This is a full-time position with an initial appointment duration of 12 months, and the possibility of further extension based on performance and funding.
Qualifications
Qualifications:
  • PhD in Computer Science, Statistics, Electrical Engineering or a related field.
  • Strong publication record in peer-reviewed venues (e.g., NeurIPS, ICML, UAI, ICLR, AAAI, AISTATS) relevant to ML or AI.
  • Strong understanding of fundamental causal inference and causal discovery algorithms.
  • Experience with the design, development and implementation of causal ML algorithms.
  • Ability to lead research projects independently from concept to implementation.
  • Capability to formulate research methodologies and manage project timeline.
  • High proficiency in Python and deep learning frameworks (e.g., PyTorch).
  • Excellent teamwork skills for working in an interdisciplinary environment involving engineers, computer scientists, and clinicians.
  • Ability to work effectively in a highly collaborative, multi-lab environment.

Application Instructions
Applicants must submit the following documents via this Interfolio posting:
  • CV
  • Contact information for two (2) references.

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