Data-Intensive Quantum Materials Simulations Lab “DataQSim Lab”
Research Field
Dr. Johann Lüder is an assistant professor at the Department of Materials and Optoelectronic Science at National Sun Yat-sen University (NSYSU) in Kaohsiung. He develops machine learning approaches to advance computational material science methods, including theoretical X-ray spectroscopy simulation and first-principles methods such as Density Functional Theory (DFT) and Orbital-free DFT. He applies state of the art computational method to solve challenges regarding renewable energy, energy storage, catalytic conversion and molecular sensors at the nanoscale.
Our lab aims to advance technology and the well-being of humans and nature by understanding and optimizing materials, their applications and conversion processes through computational modeling. We work at the interface between physics, chemistry and data science with close experimental collaborations. Fundamental science, first principles simulations, machine learning, and data science techniques are our most used tools for scientific investigations.
- Machine learning in Materials Science, Physics and Spectroscopy
- Advancing Orbital-free density functional theory and local pseudopotentials with machine learning
- Improving Organic electrode materials for lithium and Post-lithium batteries through simulations
- Organic photovoltaic materials and degradation processes by atomistic modeling
- 3rd generation semiconductors design, optimization and application by first-principles calculations
- Computational modeling of covalent organic frameworks
- Photocatalyst and electrocatalyst modeling and optimization at the atomic scale
Maybe in the future …
- B.Sc. in Physics, Free University Berlin, Germany (Oct 2010)
- M.Sc. in Physics, Uppsala University, Sweden (Nov 2011)
- PhD, Uppsala University, Sweden (Mai 2016)
- Research Fellow, National University of Singapore, Singapore (July 2016 to July 2018)
1 Vacancy
Job Description
Explore possibilities and limitations for predicting nano materials properties with machine learning.
Preferred Intern Education Level
MSc student and above
Skill sets or Qualities
- coding in python, R or C/C++
- basic quantum mechanics
- first contact with machine learning
- UNIX/Linux
2 Vacancies
Job Description
Explore the processes that lead to the photoinduced decay of organic functional materials.
Preferred Intern Education Level
MSc student and above
Skill sets or Qualities
- coding in python, R or C/C++
- basic quantum mechanics
- first contact with machine learning
- UNIX/Linux
1 Vacancy
Job Description
In this internship you will apply your coding skills to build a AR tool. Using OpenCV, a feed from a webcam or phone will be augmented through a 3D model of atomic structure by recognizing a marker such as a QR code. You project will help students of all ages learn about the representation of atomic structure and how it affects the markup of materials.
Preferred Intern Education Level
MSc or above
Skill sets or Qualities
- python or C/C++
- OpenCV
- Git