Postdoc in Geometry-Aware Machine Learning and Topological Data Analysis at Czech Technical University in Prague
Postdoc opportunity at Czech Technical University in Prague (CTU)
in
Geometry-Aware Machine Learning & Topological Data Analysis
.
This position is part of the
EU Marie Skłodowska-Curie Actions (MSCA COFUND)
international postdoc programme
CROP
. The project focuses on building machine learning methods that are more
interpretable
,
robust on small and noisy data
, and aware of
geometric
and
topological structure
in scientific datasets.
Applicants can shape a short proposal around one of three directions:
•
Scientific Machine Learning
•
Topological Data Analysis & Machine Learning
•
Dimensionality reduction preserving topological structure
Example topics mentioned in the flyer include inverse problems, PDEs on graphs in high dimensions, graph physics-informed DeepONet, Eikonal/Hamilton–Jacobi equations, inaudible sound, SERS (Raman scattering), cell/morphology dynamics, LLM representation-space transforms, high-dimensional analytical chemistry, EEG, spatial transcriptomics, 3D turbulence, metabolomics, topological autoencoders, graph representation, differentiable persistence, and manifold learning.
Eligibility highlights:
PhD in hand or close to defending; less than 8 years of research experience since the PhD; less than 12 months in Czechia in the last 3 years; prior studies compatible with the topic.
Funding:
competitive salary equivalent to a standard MSCA individual postdoc fellowship.
Deadline:
31 August 2026.
How to apply:
prepare your own proposal, consult the supervisor before applying, and submit via the official form linked in the flyer. Early contact is encouraged.
Supervisor:
Assist. Prof. Jooyoung Hahn, Department of Software Engineering, Faculty of Nuclear Sciences and Physical Engineering, CTU Prague.
Secondment partners:
University of Warsaw (Poland), ISTA Austria (Austria), KAIST (Republic of Korea), plus industry partners in Republic of Korea.
