Postdoctoral Position Openings
AIDL Lab - Efficient Applied AI
Columbia University in the City of New York
Zoran Kostic, Professor of Professional Practice, zk2172(at)columbia.edu
Electrical Engineering Department, Data Sciences Institute
Columbia University in the City of New York
Multiple Postdoctoral Positions Open as of Nov. 1, 2024
Efficient Applied AI
Columbia University in the City of New York - AIDL Lab
Applications of AI with focus on real time 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 Nov. 2024).
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.