Post-Doctoral Associate - Edge AI

New York University; Al Ruways Industrial City; On-site; AED 28,000 - 36,000; Full time

Job summary

New York University is offering a Post-Doctoral Associate position in Abu Dhabi focusing on research in Edge AI. This role combines theoretical research with system deployment across diverse applications.

Ideal candidates will have a PhD in a relevant field and strong backgrounds in machine learning, AI, and computer systems. Responsibilities include conducting innovative research, designing AI models, and collaborating within a multidisciplinary team.

Qualifications

• PhD required with strong experience in ML, AI, or computer engineering.
• Strong publication record in AI or ML conferences.
• Hands-on experience with PyTorch and TensorFlow necessary.

Responsibilities

• Conduct high-quality research in Edge-AI and related fields.
• Design and evaluate AI models optimized for performance.
• Collaborate with multidisciplinary teams in research environments.

Skills

Edge-AI

Machine learning systems

PyTorch

TensorFlow

Generative AI

Robotics AI

Autonomous systems

Cyber-physical systems

Education

PhD in Computer Engineering, Computer Science, or related field

Tools

MLOps

Hallucination mitigation

AI safety

Job description

Job Snapshot

Post-Doctoral Associate - Edge AI. Location: Abu Dhabi, United Arab Emirates. Industry: Higher Education. Function: R and D-Science. Experience: PhD with strong AI, ML, systems, or computer engineering research experience. Job Type: Full-time.

Job Details

• Country: United Arab Emirates
• City: Abu Dhabi
• Industry: Higher Education
• Function: R and D-Science
• Salary: 28,000-36,000 (estimated; confirm final offer with employer)
• Gender: Any
• Candidate Nationality: Any
• Job Type: Full-time

Role Context

The Post-Doctoral Associate - Edge AI will join the eBRAIN Lab within the Division of Engineering at New York University Abu Dhabi and support advanced research in efficient, secure, and intelligent AI systems. The position focuses on bridging theoretical research with full-system deployment, enabling solutions that can operate in resource-constrained environments such as autonomous systems, robotics, healthcare platforms, smart cities, UAVs, UGVs, wearables, and IoT-edge devices. This role combines scientific innovation, system-level optimization, real-world prototyping, and practical AI safety considerations.

Key Responsibilities

• Conduct high-quality research and development in next-generation Edge-AI, Embodied-AI, Agentic-AI, tiny-LLMs, tiny-VLMs, tiny-VLAs, and robust Generative AI systems.
• Design and evaluate AI models and full-system prototypes optimized for performance, energy efficiency, robustness, safety, security, and deployment feasibility.
• Investigate methods to reduce hallucination risks, strengthen AI alignment, improve trustworthy AI behavior, and support secure machine learning systems.
• Develop practical AI and ML solutions using PyTorch and TensorFlow for research-grade and prototype-level implementation.
• Work on multimodal AI systems, RAG pipelines, agentic frameworks, tinyML approaches, and efficient inference methods for edge and embodied applications.
• Build, test, optimize, and document prototypes for real-world use cases across robotics, autonomous platforms, healthcare, smart environments, and cyber-physical systems.
• Contribute to IP generation, patent development, technical reports, research documentation, and high-quality publications in leading AI, ML, robotics, and systems conferences.
• Collaborate with PhD-level scientists, research engineers, graduate researchers, undergraduate students, and industry partners in a multidisciplinary research environment.
• Support experimental validation, cross-validation, benchmarking, system testing, and performance analysis of AI models and hardware-software solutions.
• Prepare research summaries, technical presentations, grant-support material, manuscripts, and project progress documentation.
• Participate in lab discussions, R&D planning, scientific review, and collaborative problem-solving across AI, computer engineering, embedded systems, and machine learning security.
• Maintain strong research standards while supporting NYU Abu Dhabi's focus on world-class scholarship, innovation, and responsible technology development.

Ideal Profile

• PhD in Computer Engineering, Computer Science, Artificial Intelligence, Machine Learning, or a closely related field.
• Strong research background in ML, AI, DNNs, LLMs, VLMs, multimodal LLMs, RAG, Agentic-AI systems, tinyML, and practical AI system development.
• Hands-on experience with PyTorch, TensorFlow, ML prototyping, model evaluation, system deployment, and AI performance optimization.
• Solid understanding of AI systems for edge computing, embodied intelligence, robotics, autonomous systems, or resource-constrained environments.
• Knowledge of MLOps, privacy-preserving ML, robust computing, secure AI, hallucination mitigation, alignment, or machine learning security is highly valuable.
• Strong publication record in top-tier or highly ranked AI, ML, robotics, systems, or computer engineering conferences and journals.
• Industrial R&D experience, patent exposure, or experience converting research ideas into deployable prototypes will be an advantage.
• Excellent analytical thinking, organization, technical writing, communication, and problem-solving skills.
• Ability to work independently while contributing positively to a collaborative international research team.
• High motivation to pursue world-class research with practical value for safe, green, efficient, and trustworthy intelligent systems.

Skills Set

• Edge-AI
• Embodied-AI
• Generative AI
• Agentic-AI systems
• Tiny-LLMs
• Tiny-VLMs
• Tiny-VLAs
• Multimodal LLMs
• Retrieval-Augmented Generation
• Deep neural networks
• Machine learning systems
• TinyML
• PyTorch
• TensorFlow
• AI prototyping
• MLOps
• Robust AI
• AI safety
• AI security
• Hallucination mitigation
• Alignment research
• Privacy-preserving machine learning
• ML security
• Energy-efficient AI
• Embedded AI systems
• Robotics AI
• Autonomous systems
• Cyber-physical systems
• Research publications
• Patent development
• Technical writing