Ph.D. Opportunities in Artificial Intelligence | Stevens Institute of Technology – Fall 2026

Stevens Institute of Technology

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Description

The Stevens Institute of Technology, located in the United States, is pleased to announce the availability of Ph.D. positions in Artificial Intelligence (AI) for motivated and academically accomplished candidates. Applications are invited from individuals who demonstrate a strong commitment to research excellence and a genuine interest in advancing knowledge in AI and machine learning. Selected candidates will have the opportunity to work under the supervision of Associate Professor Yue Ning, an established researcher in the field of AI, data science, and computational modeling. Successful applicants will join a dynamic, collaborative, and interdisciplinary research team that engages with challenging, real-world problems. The lab emphasizes innovative approaches to data-driven problem solving, integrating insights from computer science, engineering, and related domains. Candidates will gain experience in state-of-the-art AI techniques, programming, and analytical methods, preparing them for impactful contributions in both academia and industry. This program offers a platform for students to develop research skills, publish in top-tier venues, and build professional networks across disciplines and institutions. 

Responsibilities

Research Opportunities

Students joining the lab of Associate Professor Yue Ning at the Stevens Institute of Technology will have the opportunity to participate in a rich and interdisciplinary research environment, contributing to cutting-edge advancements in Artificial Intelligence (AI) and Machine Learning (ML). The lab encourages inquiry into complex and impactful problems, providing students with hands-on experience in both theoretical and applied research. Key research opportunities include:

1. Investigating challenging research questions using large-scale and real-world datasets from diverse domains.
2. Designing and implementing innovative AI and ML algorithms to solve practical problems.
3. Engaging in cross-disciplinary collaboration with researchers from computer science, engineering, data science, and other related fields.
4. Contributing to peer-reviewed publications in top-tier academic conferences and journals.
5. Building a strong professional network through collaborations, conferences, and workshops.
6. Applying advanced analytical techniques to extract insights from complex data.
7. Gaining proficiency in programming and software development for AI research applications.
8. Developing expertise in data modeling, statistical analysis, and computational frameworks.
9. Working on projects that address real-world societal and industrial challenges.
10. Learning to critically evaluate research methodologies and experimental results.
11. Participating in team-based projects that foster creativity and problem-solving skills.
12. Exploring novel approaches in deep learning, reinforcement learning, and predictive modeling.
13. Leveraging high-performance computing and cloud resources for large-scale experiments.
14. Contributing to open-source research tools and software libraries.
15. Enhancing skills in scientific communication, including writing papers and presenting findings.
16. Receiving mentorship in career development and research ethics.
17. Cultivating the ability to independently drive research projects from conception to completion.
18. Engaging in inter-institutional collaborations to expand research perspectives.
19. Acquiring practical experience in data preprocessing, feature engineering, and algorithm optimization.
20. Preparing for impactful careers in academia, industry, and AI-driven innovation sectors.

This comprehensive set of research opportunities ensures that students gain technical expertise, academic rigor, and professional readiness to excel in the field of AI and machine learning. 

Qualification

Candidate Profile

The lab of Associate Professor Yue Ning at the Stevens Institute of Technology seeks highly motivated and talented individuals for its Ph.D. program in Artificial Intelligence (AI). Ideal candidates are expected to meet the following qualifications and demonstrate strong potential for advanced research:

• Possess a strong academic background in Computer Science, Data Science, Engineering, or closely related disciplines.
• Demonstrate proficiency in programming languages such as Python, Java, C++, or R.
• Exhibit strong quantitative and analytical skills applicable to AI and data-driven research.
• Prior experience in AI, Machine Learning (ML), or data modeling is considered an advantage.
• Ability to independently conceptualize and execute research projects from start to finish.
• Demonstrate curiosity and intellectual initiative in exploring challenging research problems.
• Show capability to work collaboratively within a multidisciplinary research team.
• Experience in data preprocessing, feature engineering, and algorithm implementation is beneficial.
• Strong understanding of statistical methods and computational techniques used in AI research.
• Demonstrate problem-solving skills in applied and theoretical research contexts.
• Ability to critically evaluate scientific literature and integrate research insights.
• Strong oral and written communication skills for presenting research findings effectively.
• Experience with software frameworks and libraries relevant to AI and ML, such as TensorFlow or PyTorch.
• Willingness to engage in collaborative projects across institutions or research domains.
• Capacity to adapt to new tools, technologies, and methodologies in a fast-evolving field.
• Strong commitment to scientific rigor, ethical research practices, and reproducibility.
• Motivation to publish research findings in high-quality journals and conferences.
• Ability to mentor or collaborate with junior team members when appropriate.
• Demonstrate time management and project organization skills in research-intensive environments.
• A proactive attitude toward learning and professional development in AI, ML, and related technologies.

This candidate profile ensures that selected students are well-prepared to thrive in a demanding research environment and contribute significantly to innovative AI projects. 

Application Process

Interested candidates are invited to explore the Yue Ning Lab website for detailed information regarding the Ph.D. program and application instructions: 

Submit Initial Application: Fill out the Google Form linked in the announcement. (If you can’t access the form,)

Alternatively, send your application materials by email to yue.ning@stevens.edu with the subject: PhD application – [Term] – [Your Name]

Materials to include: name, email, degree(s), GPA, CV, personal statement, transcripts, and reference contacts.

Review Process: Applications are reviewed on a rolling basis until the positions are filled. Submitting early may be beneficial since there is no fixed application deadline in the circular.

Formal University Application: In addition to the Google Form/email, all candidates must apply via Stevens Institute of Technology’s official PhD admissions portal.