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. Adrián Fernández Amil
neuroscience, synthetic cognition, hierarchical reinforcement learning, world models, spatial navigation, robotics
Dr. Alexander Hoyle
natural language processing, computational social science, human-centered evaluation
Dr. Anja Adamov
machine learning, computational biology, representation learning, multi-omics, software development
Dr. Anna Hedström
interpretable machine learning, ai safety, post-training, emergent misalignment
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. Giuseppe Chindemi
multi-agent reinforcement learning, computer vision, computational neuroscience, high-performance simulation
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. Joel Oskarsson
earth science, probabilistic machine learning, AI for science, spatio-temporal modeling
Dr. Luca Viano
reinforcement Learning, Control, LLM post-training, robotics
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. Oskar Kviman
generative modeling, tumor progression, flow matching, diffusion models, statistical inference
Dr. Sunghwan Hong
computer vision, scene reconstruction, scene understanding, vision-language models, 3D vision
