Max Planck Institute for the Structure and Dynamics of Matter – Ultrafast Electron Diffraction Experiments and Software for Experimental Control

Ultrafast electron diffraction (UED) is an established tool to record atomic motion on very short timescales well below one picosecond. The sample, typically less than 100 nanometers thick, is first excited with an optical pump-pulse leading to the rearrangement of its atomic or molecular constituents. Subsequently, the structural changes are probed by a short electron […]

Read More
Laser Annealing is Full-Scale Electron Detectors

The primary objective of the Globalink research project is to develop and implement advanced techniques for the detection of energetic electrons in silicon detectors, with a focus on overcoming the limitations of traditional detection methods and mitigating radiation damage. We will try to investigate whether laser annealing and pixel migration techniques can effectively mitigate radiation […]

Read More
Quantum super-resolving lenses

Super-resolving lenses are able to beat the standard diffraction limit in optics and image objects smaller that the wavelength of light. Such devices are already important in areas such as the bio-medical sciences, and but could also find application in the emerging field of quantum technology by providing a way of coupling qubits with high […]

Read More
Detecting atopic dermatitis with impedance measurements

This research improves atopic dermatitis (AD) detection, a common type of eczema, which affects about 20% of Canadians. AD causes dry and inflamed skin due to a lack of a key protein, making the skin barrier less effective against moisture loss and allergens. Current treatments like moisturizers and steroids can have side effects, and diagnosing […]

Read More
Enhancing Autonomous Driving using Multiple Large Language Models (MLLMs)

Nowadays, leveraging advanced technologies like Generative Artificial Intelligence (AI), particularly Large Language Models (LLMs) such as GPT, holds promise in revolutionizing safety measures and resource optimization. However, while these general-purpose LLMs excel in various tasks, they may lack context specificity. Domain-specific LLMs, such as those tailored for biomedicine and transportation, are emerging to address this […]

Read More
Sea Ice thickness distribution modeling

The aim of this internship is to carry out an analysis of the equations modelling the distribution function of sea ice thickness, particularly in the case of large deformations such as ridges. Among other things, we will be interested in the numerical solutions of these equations, their stability and their consistency.

Read More
2D material band-gap prediction by machine learning

Two-dimensional (2D) semiconductor materials are materials with thickness on the atomic scale that provide unique properties compared to their 3D counterparts. One important property of semiconductors is their band gap, which dictates how the semiconductor material will behave. However, manufacturing and testing 2D semiconductors can be costly and difficult, so the ability to predict the […]

Read More
Integrating graph-based data management into materials acceleration platforms

This research project aims to significantly improve the way data are managed in a specific self-driving laboratory in the AUTODIAL group of Prof. Hattrick-Simpers at the University of Toronto, focusing on discovering new materials that are resistant to corrosion. This class of labs, known as Self-driving labs (SDL) or Materials Acceleration Platforms (MAPs), use advanced […]

Read More