Erik
Löffelholz
DeepTech R&D engineer with a background in mathematical physics. I work in geometric machine learning, differentiable physics, and graph neural networks, and I build differentiable, GPU-accelerated systems in PyTorch, from physics simulators to generative models written from scratch. The same variational and geometric ideas I study in quantum field theory tend to show up in that engineering work. Open to fully remote ML engineering, R&D, and quantitative research roles across the EU.
01Skills & Infrastructure Stack
SK-01 Core Frameworks
SK-02 Core Mathematics
SK-03 Languages & Graphics
SK-04 Infrastructure & Tools
02Fields of Work
Quantum Field Theory & Holography
My core work is in quantum field theory in curved backgrounds and the AdS/CFT correspondence: the Sine-Gordon model and its vertex operators in two-dimensional Euclidean Anti-de Sitter space, their holographic renormalization, anomalous dimensions, and the structure of the dual boundary theory.
Read more →Differentiable Physics
I develop mesh-based differentiable physics simulators entirely in PyTorch, where mesh topology initializes particle-spring systems and physical forces come from energy-based formulations. The whole thing is differentiable, so you can optimize through the dynamics with gradients.
View project →Geometric Graph ML
A parallel interest: energy-based generative modeling of geometric graphs in 3D, like branching morphologies and meshes. It combines topological reasoning, geometric invariance, and structural priors in discrete-continuous learning frameworks.
Learn more →03Research Directions
AdS/CFT in Two Dimensions
Holographic duality between bulk fields in Euclidean AdS₂ and boundary conformal operators.
Vertex Operators & Sine-Gordon
Observables of an integrable bulk theory in hyperbolic space and their boundary duals.
Holographic Renormalization
Renormalized correlators, anomalous dimensions, and the renormalization-group flow.
Geometric & Differentiable ML
A parallel direction: energy-based modeling of geometric graphs and differentiable physics.
04Recent Writing