Explore Postdoctoral Opportunities in Efficient Applied AI at Columbia University’s AIDL Lab
Columbia University’s Advanced Intelligent Data Lab (AIDL), led by Professor Zoran Kostic, is inviting applications for multiple postdoctoral positions in Efficient Applied AI. These positions will begin in June 2025 and are expected to last two to three years. Candidates should hold a Ph.D. in Computer Science, Electrical Engineering, Data Sciences, or related fields, and be passionate about real-world applications of cutting-edge AI.
About the AIDL Lab
Based in Columbia University’s Electrical Engineering Department and the Data Sciences Institute, the AIDL Lab is at the forefront of developing AI technologies that are efficient and applicable in real-time settings. The lab focuses on conceptual modeling, data experimentation, prototyping, and real-time inferencing, with applications in smart cities, healthcare, and environmental monitoring.
Research Areas
1. AI Applications in Smart Cities
- AI-supported Digital Twins for learning and inference
- Data and video collection for urban intersections
- Experiments using real-time smart city testbeds
- Data/video pre-processing for machine and deep learning models
- Object detection and tracking
- Small object detection with a focus on speed and accuracy
- Seamless integration with operational systems
2. AI-Assisted Healthcare Using Speech and Language
- Detection of patient health decline
- Analysis of patient-caregiver interactions
- Aggregation of audio, textual, and medical record data
- Secure RAG systems for LLM-based inference
- Development and assessment of synthetic datasets
3. Environmental Studies: Water Pollution and Microplastics
- Dataset collection, curation, and annotation
- AI model development for identifying exotic environmental materials
- Prototype building in collaboration with environmental science institutions
4. AI in Surgery
- Enhancing robot-assisted surgeries using video data
- Improving training systems for surgical residents
- Real-time anomaly detection during procedures
- Decision-making augmentation via LLMs
- Instrument and phase tracking in surgical processes
5. General Research Themes
- Modeling, simulation, and system emulation
- Data preparation for deep learning
- Real-time system constraints in ML and DL
- Minimizing latency in sensing, communication, and processing
- Distributed, federated, and collaborative ML approaches
Qualifications
- A completed Ph.D. in a relevant field such as Computer Science, Electrical Engineering, or Data Sciences
- Proficiency in machine/deep learning, signal and video processing, edge computing, communication systems, and real-time processing
- Experience with simulation software, parallel and GPU computing, and cloud services
- Interest in mentoring, teaching, and contributing to grant proposals
Eligibility
- Ph.D. completed within five years before the appointment start date
- Compliance with employment eligibility standards for international candidates
Application Process
To apply, interested candidates must complete an internal questionnaire and send an email application. The email should include a CV and a statement of interest outlining relevant experience and alignment with research goals. Email subject lines should follow the format: “postdoctoral application EEAI – [Your Name]”.
Funding and Collaboration
These positions are supported by multiple high-profile organizations including national science and health agencies. Collaboration opportunities span various departments within Columbia’s School of Engineering and the Data Sciences Institute, offering a rich multidisciplinary research environment.
Contact Information
Professor Zoran Kostic
Professor of Professional Practice
Electrical Engineering Department
Data Sciences Institute
Columbia University in the City of New York
Email: zk2172@columbia.edu
