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Haggai Maron

5 days ago

Postdoc in Geometric Deep Learning for Neural Artifacts and Weight-Space Learning Technion in Israel

Degree Level

Postdoc

Field of study

Computer Science

Funding

Funded postdoc position supported by the announcer's new ERC Starting Grant, GDLNeuralArt.

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Country

Israel

University

Technion

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Keywords

Computer Science
Electrical Engineering
Deep Learning
Mathematics
Symmetry
Statistics
ML
Postdoc pos

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About this position

Haggai Maron is hiring a postdoc in his group at the Technion, supported by a new ERC Starting Grant called GDLNeuralArt.

The research focus is on learning on weight spaces, internal representations, and gradient spaces—treating neural networks and their artifacts as data. The post highlights work on trained checkpoints, activations, gradients, and the structure of neural network spaces, including neuron permutations, scaling symmetries, and multiple architectures.

This opportunity is especially relevant for candidates interested in geometric deep learning, symmetry-aware learning, and principled methods for analyzing neural artifacts. The ideal candidate should have 5+ first-author papers at top ML venues (NeurIPS/ICML/ICLR or similar), strong mathematical foundations, solid coding skills, and preferably experience with geometric deep learning or learning with symmetries.

The position is based in Israel at the Technion and is described as a key role in the lab, with involvement in multiple projects and mentoring PhD and MSc students. To apply, email [email protected] with a CV and a short note about your interest. More information is linked via the research overview and workshop pages.

Funding details

Funded postdoc position supported by the announcer's new ERC Starting Grant, GDLNeuralArt.

What's required

5+ first-author papers at top ML venues such as NeurIPS, ICML, or ICLR (or similar); strong mathematical foundations; solid coding skills; experience in geometric deep learning or learning with symmetries is a plus.

How to apply

Email Haggai Maron with your CV and a short note explaining what excites you about the research area.

More information can be found here

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