Postdoctoral Position Openings

Multiple Postdoctoral Positions starting in 2025

Efficient Applied AI

Columbia University in the City of New York - AIDL Lab

Applications of AI with focus on inference.

Conceptual studies, modeling, experimentation on real life platforms, prototyping, real-time inferencing.

Smart cities, healthcare/medicine, environmental studies.

• For candidates with a PhD degree in CS, EE, Data Sciences, or a related major.

• Two-three years, starting immediately (active as of June 2025).

• Mentored by Prof. Zoran Kostic, Columbia University Electrical Engineering Department and Data Sciences Institute.

• Projects funded by the NSF ERC Smart Streetscapes, NSF Cyber Physical Systems Program, NIH, NOAA.

• Collaboration across multiple departments at Columbia School of Engineering and Data Sciences Institute.

• (This page) For a detailed description and instructions on how to express interest, see https://www.aidl.ee.columbia.edu/postdoc.

Research Topics in  Efficient Applied AI

• AI applications in smart cities

  • Digital Twins supported by AI - learning and inference

  • Data/video acquisition for smart city intersections.

  • Experiments on the real time COSMOS testbed in NYC.

  • Data/video pre-processing/conditioning in support of ML/DL methods.

  • Object detection and tracking in smart-city applications.

  • Optimization of accuracy and speed for small object detection.

  • Integration with live systems.

• Using speech and language for AI-assisted healthcare

  • Detecting decline in patients' health

  • Interactions between patients and healthcare providers - tracking and analysis

  • Multimodal data aggregation - audio, text, EMRs

  • Secure RAGs for LLM-based inferencing

  • Synthetic datasets - creation and evaluation

• Search and elimination of water pollution and microplastics

  • Collection, curation, annotation of new datasets

  • Exotic materials and objects - AI model development and evaluation

  • Development of a prototype in collaboration with Lamont institute

• AI in surgery

  • Improving robot assisted surgery using data extracted from videos

  • Surgical training

  • Anomaly detection

  • LLM-assisted decision making

  • Surgical phase and instrument detection and tracking

• Generic

  • Modeling, simulation and emulation.

  • Data conditioning for deep-learning based processing

  • Real-time considerations for ML/DL inference.

  • Low latency for sensor data acquisition, communications and networking.

  • Distributed, federated, collaborative learning and inference.

Qualifications:

• PhD degree in Computer Science, Electrical Engineering, Data Sciences, or related disciplines.

• Expertise in several of the following topics: Machine Learning and Deep Learning; Signal, Image and Video Processing; Edge, Distributed and Collaborative Computing; Communications and Networking; Real-Time Systems;  IC design. 

• Hands-on experience with software development, simulations, parallel/GPU computation, and cloud computing services. 

• Interest in writing grant proposals, mentoring junior researchers, teaching. 

Eligibility: 

• Ph.D. in a relevant field must be completed before beginning the appointment and no more than five years before the application deadline.

• Foreign citizens must be able to satisfy the employment eligibility requirements. 

To Express Interest:

• Populate "Postdoctoral Position Questionnaire" at the bottom of the page (also here: https://forms.gle/LdvaUiRUJk2TF2ph6). 

• Send email to ee-postdocAI@ee.columbia.edu. Use the email subject line “postdoctoral application EEAI - <your name>”. Attach CV and a statement of interest with description of how your experiences and expertise are relevant to the postdoctoral research topics.

Links:

• Prof. Zoran Kostic https://datascience.columbia.edu/people/zoran-kostic/   

• Research https://www.aidl.ee.columbia.edu/ 

• COSMOS Smart-City Research: https://cosmos-lab.org/experimentation/smart-city-intersections/ 

• NSF Engineering Research Center for Smart Streetscapes: https://cs3-erc.org/

• NIH R01 https://reporter.nih.gov/search/6pfuGg2sN0iDm0oHUSFAQA/project-details/10638400 

• NOAA https://www.engineering.columbia.edu/about/news/combatting-microplastics-ai-real-time-monitoring 

Positions closed as of 10/01/2022 

Postdoctoral Position - Machine Learning and Digital Twins, Columbia University

• For candidates with a PhD degree in CS, EE, Data Sciences, or a related major.

• Two years, starting before September 2022.

• Mentored by Prof. Zoran Kostic, Columbia University Electrical Engineering Department and Data Sciences Institute.

• Funded by the NSF Cyber Physical Systems Program.

• Collaboration across multiple departments at Columbia School of Engineering and Data Sciences Institute.

• (This page) For detailed description and instructions on how to express interest, see https://www.aidl.ee.columbia.edu/postdoc.

Research Topics: 

• Theory and practice - machine learning (ML), deep learning (DL) and digital twins.

• How ML/DL methods enable digital twins.

• How digital twins improve learning and inference in ML/DL models.

• Reinforcement learning.

• Modeling, simulation and emulation.

• Distributed, federated, collaborative learning and inference.

• Real-time considerations for ML/DL inference.

• Low latency for sensor data acquisition, communications and networking.

• Integration of privacy and security into data management.

• Applications in smart cities:

  • Data/video acquisition for smart city intersections.

  • Data/video pre-processing/conditioning in support of ML/DL methods.

  • Object detection and tracking in smart-city applications.

  • Optimization of accuracy and speed for small object detection.

  • Integration with live systems.