Two-year Postdoc (f/m/d) in
Machine Learning for Quantum Computing and Simulation of Quantum Matter
Official job advertisement: Link
Project
The successful candidate (f/m/d) will be part of the interdisciplinary DRESDEN-concept Research Group AI 4 Quantum which is currently established at the Center for Advanced Systems Understanding (CASUS) of the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) and the Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig (ScaDS.AI) at the Center for Interdisciplinary Digital Sciences (CIDS) of the TU Dresden.
You will develop and apply novel deep machine learning (ML) methods, neural network and artificial intelligence approaches to extend the reach of accurate computational methods to study strongly correlated quantum matter.
The focus will be the development of novel Neural Quantum States, deep learning approaches to design quantum computing (QC) algorithms, and AI and ML techniques to improve state-of-the-art quantum Monte Carlo methods.
You can find more information on the planned research of AI 4 Quantum here.
Requirements
The specific form of the project will be customized to align with the specific expertise and interests of the selected candidate. Consequently, candidates from diverse scientific backgrounds are encouraged to apply. Key qualifications include coding experience (preferably in Python/Julia and C++ or Fortran), a strong mathematical foundation within a scientific context, and prior experience with algorithm development or implementation.
Additionally, for this postdoctoral position, relevant experience in ML/AI approaches, method or algorithm development related to solve natural science problems, quantum information, or quantum computing is highly desired. However, we mainly seek a highly motivated, committed, and enthusiastic candidate.
Candidates are highly encouraged and welcome to suggest and pursue their own ideas within the general scope of ‘AI for Quantum Computing and Quantum Matter.’
Candidates will gain (or complement and extend existing) highly sought-after skills valued in both industry and academia, including expertise in novel machine learning/AI approaches, computational chemistry/physics, quantum algorithms, and software engineering, all within a collaborative and supportive environment.
Additionally, the PI has a successful history in acquiring highly competitive third-party funding (Marie Curie, Vinnova, BMBF). As such, we are also open and interested in assisting with applications for competitive postdoctoral and future junior research group funding options.
About us
We are a newly established (since Dec. 2024) DRESDEN-concept Research Group at CASUS of the HZDR and ScaDS.AI at the CIDS of the TU Dresden.
We are building the AI 4 Quantum research group that focuses on developing a synergistic HPC+QC approach aided by novel artificial intelligence/deep machine learning methods to enable the computational study of complex quantum systems relevant to the green energy transition.
Additionally, our current research focuses on developing innovative quantum Monte Carlo methods and novel quantum computing algorithms to enable realistic electronic structure calculations for strongly correlated electron problems on high-performance computing hardware and near-term quantum computing devices.
You can find more information about us, our research, and the PI here on our homepage.
We are dedicated to fostering a supportive, inclusive research environment encouraging scientific excellence. We welcome applications from individuals of all backgrounds and expressly invite members of underrepresented groups to apply.
If you are interested please apply via HZDR online-application-system found on the bottom of the official job advertisement: Link
Only applications via the application system can be considered.
If you have any questions, please reach out to me via:
email.
Application procedure
Candidates are screened until the above-mentioned application deadline. Suitable candidates are then invited to an online interview. Rejected candidates will be informed about the decision around or a few days after the application deadline.
