📝 Ready to apply? Submit your details using the Google Form. Email may be missed—form submissions are reviewed regularly.
Join ARIA Lab
We are recruiting Ph.D., MS, and Undergraduate students for research positions in the ARIA Lab. Ph.D. positions are fully funded; MS/Undergrad students can earn research credit. Self-funded visiting students/scholars also welcome.
| University | NJIT (R1), Newark NJ |
| Start Date | Spring/Fall 2026 |
| Ph.D. Funding | RA/TA (2 fully funded positions + self-funded) |
| MS/Undergrad | Research credit available |
Open Research Projects
| Project | Level |
|---|---|
| Neurosymbolic AI Integrate symbolic reasoning with deep learning for transparent, interpretable AI systems. |
Ph.D. Lead MS/Undergrad |
| Neural Combinatorial Optimization Learn to build neural solvers for combinatorial problems. |
Ph.D. Lead MS/Undergrad |
| DRL for Graph Optimization Apply deep reinforcement learning to graph-based combinatorial optimization problems. |
MS / Undergrad |
| Application Domains Apply neurosymbolic methods to vision, video understanding, and multimodal reasoning. |
All Levels |
Who Can Apply
Ph.D. Students
- Up to 2 TA/RA positions for Spring 2026
- Strong research background in AI/ML required
- Must also apply through NJIT admissions portal
MS Students
- Research credit via CS 700B (Master’s Project) or CS 701B (Master’s Thesis)
- Exceptional contributors may continue long-term
Undergraduates
- Research credit via CS 488 (Independent Study)
- Outstanding contributors may extend to full research projects
What You’ll Do
- Prototype novel algorithms and run ablations at scale
- Build reliable, reproducible research pipelines
- Design experiments, interpret results, and iterate
- Apply methods to vision/video and multimodal tasks
- Work on projects that can be submitted to top-tier AI/ML venues
What You’ll Bring
| Essential | Nice to Have |
|---|---|
| Strong ML/AI fundamentals | Neurosymbolic AI |
| Python + PyTorch/JAX (optional PyTorch Geometric) | Combinatorial/constrained optimization |
| Algorithms & probability | Deep reinforcement learning |
| Git, testing, reproducibility | Graphical models, probabilistic circuits |
Lab Resources
- Funding: Fully funded Ph.D. (RA/TA), research credit for MS/Undergrad
- Compute: University Wulver GPU cluster + lab GPUs
- Mentorship: Direct guidance, publication opportunities at top venues (NeurIPS, ICML, ICLR, AAAI, KDD, CIKM, etc.)
Why NJIT
- Ranked #72 in the U.S. for AI & Machine Learning (CSRankings)
- Ranked #80 Graduate School for Computer Science (U.S. News & World Report)
- Ranked #84 among National Universities and #42 among Top Public Universities (U.S. News & World Report)
- Located in Newark, NJ, part of the NYC metropolitan area
- ~30 minutes from Manhattan; strong industry connections
Located in Newark, NJ—part of the NYC metropolitan area with strong industry connections.
FAQ
Q: Should I email you directly?
No, please use the Google Form. Emails are often missed, but form submissions are reviewed regularly.
Q: Do I need publications to apply?
Not required, but helpful. We value demonstrated research potential and strong fundamentals.
Q: Can international students apply?
Yes! Ph.D. positions are fully funded regardless of citizenship.
Q: What’s the application timeline?
We review applications on a rolling basis. Apply early for best consideration.
Ready to Apply?
Submit your details via Google Form. For Ph.D., also apply through NJIT admissions and mention my name in your Statement of Purpose.