Post-Doctoral Fellows
Post-Docs that are listed here are recipients of the highly competitive ETH AI Center Post-Doctoral Fellowship. They are focused on advancing interdisciplinary AI research and are connected with two or more research groups of the ETH AI Center.
Dr. Alexander Hoyle
natural language processing, computational social science, human-centered evaluation
Dr. Bruce Lee
reinforcement learning, control systems, robotics, statistical learning theory
Dr. Christina Humer
interpretable machine learning, climate change, material discovery, visual analytics
Dr. Diane Duroux
precision medicine, graph theory, recommender system, multimodal learning
Dr. Fanny Lehmann
scientific machine learning, physics-based deep learning, high-performance simulation, Earth sciences
Dr. Georgios Kissas
scientific machine learning, operator learning, data-driven model discovery, precision medicine
Dr. Gonçalo Guiomar
synthetic cognition, computational neuroscience, reinforcement learning, large language models, external page generative art
Dr. Heejin Do
natural language processing, AI in education, human-centered AI, evaluation and interpretability, large language models
Dr. Ido Hakimi
natural language processing, optimization, efficient machine learning systems
Dr. Marina Esteban
precision medicine, probabilistic machine learning, explainability, mechanistic modelling, multimodal learning, cancer research
Dr. Mubashara Akhtar
natural language processing, vision-language reasoning, benchmarking & evaluation.
Dr. Pei-Yu Wu
multimodal learning, explainable AI , data mining, building stock analysis
Dr. Sunghwan Hong
computer vision, scene reconstruction, scene understanding, vision-language models, 3D vision
Dr. Thomas Kleine Buening
reinforcement learning, preference learning, alignment, game theory
