Wissenschaftliche/-r Mitarbeiter/-in -Doktorand/-in (m/w/d)
Core
Implementing and improving equivariant Graph Neural Networks for Machine Learning Interatomic Potential (MLIP) models and modifying Boltzmann Generator models for non-canonical amino acids within an AI for Protein Design project.
Role type
PhD researcher (scientific staff)
Builds
MLIP models and Boltzmann Generator models for protein design
Domain
Computational chemistry / AI for drug discovery
Deliverable
production ML models
Required skills
Equivariant Graph Neural Networks, Machine Learning Interatomic Potentials, Boltzmann Generator models, Neuronal Partial Differential Equations, anisotropic diffusion models, Python/C++ (implied by implementation), Quantum chemistry
Preferred skills
Diffusion-based generative machine learning models
Technologies
Graph Neural Networks, Boltzmann Generators, MLIP, Diffusion models
Responsibilities
Implementing and improving equivariant Graph Neural Networks for MLIP models, Modifying Boltzmann Generator architectures for non-canonical amino acids, Implementing quantum-chemical MLIP models, Fine-tuning Boltzmann Generator models on peptide datasets
Seniority
PhD researcher (academic position)