PostDoc Fellowships
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Hi! PARIS is offering prestigious 2-year postdoctoral fellowships for excellent early-career researchers in AI and Data Science. The fellowship must be hosted by a research group from IP Paris or HEC Paris and propose a research project aligned with the mission of Hi! PARIS.
Who Can Apply?
Our fellowships are open to researchers who have recently completed their PhD and have a strong background in Artificial Intelligence and Data Science.
Our Postdoctoral Researchers
Project Title: A Distributional Perspective on (Meta) Inverse Reinforcement: Learning with Applications in Soft Robotics.
Project Overview: Mohamed’s research explores inverse reinforcement learning (IRL) from a distributional perspective. The focus is on meta-IRL, a framework that enables models to quickly adapt to new environments. This project particularly emphasizes applications in soft robotics, where the complexity and flexibility of these systems require advanced, adaptable learning methods. By understanding and improving how reinforcement learning agents learn from environments, Mohamed’s work aims to advance robotics systems that are both more responsive and adaptable to real-world tasks.
Project Title: Learning Multi-domain Graphs from Data via Graph Machine Learning: Theoretical Analysis and Applications
Project Overview: Aref is tackling one of the core challenges in graph machine learning: understanding and modeling complex, multi-domain graphs. His project centers around developing theoretical foundations for learning from these large-scale, interconnected datasets and applying these techniques across various domains. His work has implications in areas such as social networks, bioinformatics, and infrastructure networks, where data is highly interconnected and dynamic. The project aims to uncover new ways to analyze and learn from graphs, allowing for more robust and scalable machine learning solutions.
Project Title: Statistical Learning on Markov Chains
Project Overview: Carlos’s research focuses on statistical learning with a specific emphasis on Markov chains, which are foundational models used to represent stochastic processes. His project aims to develop more advanced learning algorithms to improve the modeling of time-dependent systems, with potential applications ranging from finance to engineering. By creating better tools for analyzing systems that evolve over time, Carlos is contributing to a deeper understanding of both predictable and random phenomena across various sectors.
Project Title: Large Multimodal Models for Responsible Business and Society
Project Overview: Zhang is developing multimodal models—AI systems that can integrate and analyze multiple types of data simultaneously (such as text, images, and audio). His research aims to apply these models to business and societal challenges, with a focus on responsible and ethical practices. From helping companies make data-driven decisions that promote sustainability to advancing fair AI systems, Zhang’s work is positioned at the intersection of technology, business, and ethics. His research provides a framework for building large-scale AI models that support societal and economic progress in a responsible way.
Project Title: Analyzing Fact-checks and Political Communication
Project Overview: Garima’s research is at the forefront of analyzing political communication through AI-driven fact-checking. Her work focuses on identifying and understanding patterns of misinformation and political messaging, particularly during elections and major public events. By using AI to automate and improve the accuracy of fact-checking systems, Garima aims to create tools that can help combat misinformation in real time. This project is critical in the modern age of digital communication, where rapid dissemination of false information can have widespread societal impacts.
Project Title: Enhancing Data Privacy in AI Systems through Advanced Cryptography Techniques
Project Overview: Xi Wang’s research focuses on the intersection of data privacy and artificial intelligence, particularly leveraging advanced cryptographic techniques to ensure secure and private data handling in AI systems. With the increasing use of AI in sensitive areas like healthcare, finance, and government, Xi’s work aims to develop protocols that protect individual data while still allowing AI systems to perform optimally. By employing methods such as homomorphic encryption and secure multiparty computation, Xi’s project seeks to advance privacy-preserving AI solutions that can be applied across various sectors where data security is critical.