The Helmholtz Graduate School for the Structure of Matter (DASHH) is an interdisciplinary graduate school that offers challenging PhD topics at the interface of the Natural Sciences, Applied Mathematics, Data Science and Informatics. DASHH involves several key research institutions and universities in the multifaceted city of Hamburg, Germany.
The DASHH Call for Applications 2021 for 13 exciting PhD positions is open now. DASHH is seeking highly qualified and highly motivated candidates with an excellent academic background in the Natural Sciences or in Computer Science/Mathematics as well as experience in programming.
DASHH offers data-driven interdisciplinary research topics in Physics, Engineering, Chemistry, Applied Mathematics, Informatics, and Structural Biology with a work contract e.g., at the level of the German TV-L/TV-AVH salary scheme (remuneration group 13, 100 %) for 3 years.
In order to apply you need to have a Master's Degree (or an estimated date of Graduation within the same year of application) in computer science, applied mathematics or natural sciences, preferably with an interdisciplinary training at the interface of natural and computer science or mathematics. Degrees from foreign universities and master degrees in non-research oriented study programs (Fachhochschule) might be eligible. For further information, consult the doctorate regulations (Promotionsordnung) of the respective partner university.
The PhD topics are:
- 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
Deadline: 1 December 2021.