Recognizing Excellent Early-Career Research
The MCQST PhD Award annually honors two outstanding PhD theses from the MCQST community. The prize highlights and recognizes excellence in research at an early career stage and encourages awardees to pursue a further career in science.
MCQST acknowledges exceptional theses in Quantum Science and Technology that have received a summa cum laude grade. These theses demonstrate an innovative and cutting-edge approach within the chosen topic or field of study, outstanding personal contributions, and creativity. Consideration is given to theses from various disciplines including physics, mathematics, computer science, electrical engineering, material science, and chemistry. Both LMU and TUM submissions are eligible, provided the thesis defense took place in the year 2023.
How to Apply
To apply, send the following documents in order as a single PDF document to support[at]mcqst.de:
- Abstract of thesis
- PhD certificate
- CV
- List of publications
- PDF of thesis in a separate file (or link)
- And a letter of reference by an MCQST PI. The letter should be sent directly to support[at]mcqst.de by the PI supporting the application.
Application deadline is 23 February 2024.
Questions? Contact support[at]mcqst.de.
MCQST PhD Award 2022
Matthias C. Caro
"Quantum Learning Theory" - TU Munich
Advisor:
Michael Wolf
Johannes Feldmeier
"Nonequilibrium Dynamics in Constrained Quantum Many-Body Systems" - TU Munich
Advisor:
Michael Knap
MCQST PhD Award 2021
Annabelle Bohrdt
"Probing Strongly Correlated Many-Body Systems with Quantum Simulation"
TU Munich
Advisor: Michael Knap
Benjamin Merkel
"Enhancing the Emission and Coherence of Erbium Dopants"
MPI of Quantum Optics & TU Munich
Advisor: Andreas Reiserer
MCQST PhD Award 2020
Johannes Knörzer
"Semiconductor-Based Electron Lattices for Quantum Information Processing"
LMU München & MPI of Quantum Optics
Advisor: J. Ignacio Cirac
Karen Wintersperger
"Realization of Floquet Topological Systems with Ultracold Atoms in Optical Honeycomb Lattices"
LMU München
Advisor: Monika Aidelsburger