Post-Doctoral Associate in Edge-AI and Embodied-AI Systems
Develop next-generation AI systems for energy-efficient, secure, and robust embodied and edge AI applications.
Best for:Best for AI/ML researchers with expertise in tiny-LLMs, multimodal AI, and hardware-software co-design, seeking to contribute to cutting-edge embodied AI systems with a focus on robustness and energy efficiency.
Overview
This postdoctoral position at NYU Abu Dhabi’s eBRAIN Lab focuses on advancing Edge-AI and Embodied-AI systems with tiny-LLMs, tiny-VLMs, and Agentic-AI frameworks. The role involves designing, optimizing, and deploying AI solutions for real-world applications in autonomous systems, healthcare, and robotics, with a strong emphasis on robustness, energy efficiency, and trustworthiness. Applicants will collaborate with a multidisciplinary team to prototype and test innovative solutions, contribute to high-impact publications, and drive intellectual property generation. The lab’s mission aligns with embedding secure, energy-efficient cognitive technologies into cyber-physical systems, serving humanity and the environment sustainably.
What you'll do
- Design, implement, and optimize next-generation Edge-AI and Embodied-AI systems using tiny-LLMs, tiny-VLMs, and Agentic-AI frameworks.
- Develop novel methods with strong theoretical foundations for robust, energy-efficient AI systems targeting real-world applications like autonomous systems, healthcare, and robotics.
- Prototype and test innovative AI solutions, ensuring high performance, energy efficiency, and trustworthiness.
- Collaborate with a multidisciplinary team to deploy full-system solutions in real-world environments, including cross-validation across diverse applications.
- Generate intellectual property and contribute to high-quality publications in top-tier ML/AI, Robotics, and Systems conferences.
- Work closely with PhD students, research engineers, and industry collaborators to advance R&D goals and foster a collaborative research environment.
- Engage in problem-solving, analytical thinking, and communication to drive world-class research and development in AI systems.
Requirements
Degree required: PhD in Computer Engineering, Computer Science, or a closely related field
Language: English; no specific proficiency level mentioned
Instruction: English
Publications: Highly preferred: publications in top/A* ML venues (conferences and journals)
- Extensive knowledge of ML, AI, deep neural networks (DNNs), and LLMs/VLMs, including multimodal LLMs and RAG frameworks
- Proven experience in practical AI/ML system development and prototyping
- Proficiency in ML frameworks such as PyTorch and TensorFlow
- Strong understanding of MLOps, robust/secure computing, hallucination and alignment in AI systems, and privacy-preserving ML
- Experience with tinyML and deployment of AI models in resource-constrained environments
- No explicit nationality or residency restrictions mentioned
- No age or prior experience caps mentioned
Funding & terms
How to apply
Deadline: 2026-10-28 · open until filled
Applicants must submit a cover letter, detailed CV with R&D and programming skills, full list of publications and experience, university degrees and transcripts, executive summaries of PhD/MS theses, a 1-page summary of professional experience, a 1-page R&D accomplishments and interests statement, a 1-page letter of motivation, at least 2 letters of recommendation, an R&D statement with a 2-year work plan, and download links for PhD/MS theses (if available). Applications are accepted immediately and reviewed until the position is filled.
- Cover letter
- Detailed curriculum vitae (CV) with R&D and programming skillset
- Full list of professional/industrial experience and scientific publications
- University degrees and transcripts (or letter regarding tentative completion date)
- 1-page executive summaries of PhD and MS Theses
- 1-page summary of professional experience, future growth plans
- 1-page of R&D accomplishments and interests
- 1-page letter of Motivation
- At least 2 letters of recommendation with contact information
- R&D statement with an envisioned workplan over 2 years
- Download links for PDFs of PhD and MS Theses (if available)
Contact
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