PhD Topics

Polycrystalline Materials
Quantum Dynamics
Fault Prediction
Heavy Higgs Boson
Rate-Equation Systems
Crystallographic Analysis
Quantum Simulations























PhD Topic Overview (Call open until December 1st, 2021)

DePhaCTo: Deep Learning for X-Ray Phase Contrast Tomography
Polycrystalline Materials: Automatized Diffraction Pattern Recognition for Scanning Surface X-Ray Crystallography of Polycrystalline Materials
Quantum Dynamics: Time Evolution for Quantum Dynamics with Efficient Solvers
NN4CryoEM: Identification of Macromolecules in Cryo-Electron Microscopy Reconstruction Maps Using Neuronal Networks
Fault Prediction: Board Level Fault Prediction for the Embedded Sensor Systems at the European XFEL
Heavy Higgs Boson: Search for Heavy Higgs Bosons and Axion-Like Particles with the CMS Experiment via Deep Neural Networks
Nanotomography: Machine Learning for the Automated Selection and Reconstruction of Multi-Modal Nanotomography Data of Bone-Implant Interfaces
Rate-Equation Systems: Development of Machine Learning Approaches for Solving Large Rate-Equation Systems
ML4Collider Data: Topology- and Dimensionality-Aware Learning of Physics Data
LeCASE Pr: Leveraging Continuous & Assisted Software Engineering to Study and Support Development Processes at Free Electron Lasers
Crystallographic Analysis: New Algorithmic Approaches to Macromolecular Crystallographic Analysis
LeCASE Ar: Leveraging Continuous & Assisted Software Engineering to Assess and Improve Software Artefacts' Quality at Free Electron Lasers
Quantum Simulations: Quantum Simulations of Laser-Induced Electron Diffraction using Recurrent Neural Networks