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. Anna Susmelj

Dr. Anna Susmelj

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representation learning, self-supervised learning, domain generalisation, domain adaptation, causality, biomedical imaging, computer vision

Dr. Benjamin Moseley

Dr. Benjamin Moseley

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scientific machine learning, physics-informed neural networks, differential equations


Dr. Carl Allen

Dr. Carl Allen

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theoretical machine learning, representation learning, latent variable modelling, contrastive learning

Dr. Carmen Amo Alonso

Dr. Carmen Amo Alonso

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natural language processing, control theory, autonomous systems


Dr. Christina Tsalicoglou

Dr. Christina Tsalicoglou

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3D computer vision, scientific machine learning, data-driven modeling, physics-informed neural networks

Dr. Diane Duroux

Dr. Diane Duroux

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precision medicine, graph theory, recommender system, multimodal learning


Dr. Etienne Bamas

Dr. Etienne Bamas

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theoretical computer science, approximation algorithms, learning-augmented algorithms

Dr. Fan Shi

Dr. Fan Shi

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legged robots, learning-based control, animal-like behavior in robots


Dr. Francis Engelmann

Dr. Francis Engelmann

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3D scene understanding, 3D semantic instance segmantation, 3D object detection, 3D computer vision

Dr. Georgios Kissas

Dr. Georgios Kissas

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scientific machine learning, operator learning, data-driven model discovery, precision medicine


Dr. Neda Davoudi

Dr. Neda Davoudi

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multi-modal analysis, biomedical imaging, computer vision

Dr. Shkurta Gashi

Dr. Shkurta Gashi

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machine learning, ubiquitous computing, health monitoring, multimodal learning


Dr. Theodora Kontogianni

Dr. Theodora Kontogianni

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computer vision, 3D vision, interactive learning, continual learning

Dr. Xiaoyu Zhang

Dr. Xiaoyu Zhang

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visual analytics, text&knowledge visualization, HCI, human-AI collaboration, human-centered AI, explainable AI


Dr. Xinwei Shen

Dr. Xinwei Shen

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causal inference, distributional robustness, interpretability

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