Research Assistant - 0.75 working-time

TU; Berlin; Hybrid; EUR 52,000 - 66,000; Full time

Job summary

Technische Universität Berlin invites applications for a doctoral research position focusing on physics-based algorithms for reconstructing strain and electric fields at nano-heterostructure interfaces. You'll work with 300kV S/TEM infrastructure to tackle numerical inversion of dynamical electron diffraction and analyze experimental data.

You will develop methods (4D-STEM, NBED, DPC, holography), run simulations, and validate inversion algorithms while collaborating with national and

Qualifications

• Master/Diplom in physics or related field completed by start of employment.
• Strong numerical programming and computational modeling experience.
• Experience with experimental data analysis and software development is desirable.

Responsibilities

• Develop physics-based algorithms for reconstructing strain and electric fields at nano-heterostructure interfaces.
• Contribute to dynamical electron diffraction analyses and 4D-STEM datasets.
• Simulate and analyze dynamical diffraction effects; optimize inversion algorithms.
• Apply methods to interfaces in nanostructures and domain walls in ferroelectrics.
• Acquire and analyze NBED, DPC, and off-axis holography datasets; explore ML approaches.
• Collaborate with national and international project partners in computational electron microscopy and materials science.

Skills

Numerical programming

Computational modeling

Independence / self-motivation

German / English language

ML familiarity (PyTorch or JAX)

Education

Master's degree in physics or related field

Diploma equivalent

Tools

Python

PyTorch

JAX

Numerical libraries

Job description

Your responsibility

You will contribute to the development of physics-based algorithms for the reconstruction of strain fields and electric fields at interfaces of nano-heterostructures. The focus is on the numerical inversion of dynamical electron diffraction. For your experimental work, you will have direct access to the highly specialized, state-of-the-art 300kV S/TEM infrastructure at Technische Universität Berlin.

Your tasks will include:

• Development of experimental measurement methods for acquiring dynamical electron diffraction datasets, for example using 4D-STEM or electron holographic beam tilt series.
• Simulation and analysis of dynamical diffraction effects.
• Optimization, evaluation, and validation of numerical inversion approaches, especially regarding algorithms and regularization methods.
• Application of the developed inversion methods to interfaces in nanostructures (such as semiconductor heterostructures) and domain walls in ferroelectrics.
• Acquisition and analysis of comparison datasets using Nano-Beam Electron Diffraction (NBED), Differential Phase Contrast (DPC), and Off-Axis Electron Holography
• Exploration of machine learning approaches for the reconstruction.
• Independent collaboration with national and international project partners in the fields of computational electron microscopy and materials science.

The position offers the opportunity to pursue a doctorate. For further information, please contact Dr. Laura Niermann ([e-mail]).

Your profile

• Successfully completed university degree (Master, Diplom, or equivalent) in physics or a related discipline; the degree must be completed by the start of employment.
• Very good knowledge of numerical programming, computational modeling, and their application to the analysis of experimental data.
• Experience in software development and programming (ideally in Python).
• A high degree of independence and self-motivation, as well as experience in independent scientific work desirable.
• Good knowledge of German and/or English required; willingness to acquire the respective missing language skills.
• Experience working with numerical libraries and machine learning frameworks (like PyTorch or JAX) desirable.
• Experience in (scanning) transmission electron microscopy and/or solid-state physics desirable.
• Knowledge of statistical methods as well as basic knowledge of machine learning desirable.