We offer Postdoc positions as a part of the "Beyond classical quantum machine learning for quantum physical systems" ERC project in the applied Quantum algorithms initiative in Leiden University.
The 5 year project aims to establish new theoretical and practical bridges between quantum machine learning and hard problems in quantum many-body physics, including problems in condensed matter, high energy physics and quantum control.
The research directions (and our recent papers in roughly the same direction for illustration purposes) we aim to explore include:
-development and theoretical and empirical characterization of new classes of quantum machine learning models, and identifying the role of their genuinely quantum features (https://www.nature.com/articles/s41467-023-36159-y)
-development and analysis of novel quantum algorithms for data analysis (e.g. variants of quantum topological data analysis) with provable separations (https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.5.010319))
-characterization of classes of learning problems which can exhibit quantum advantages over any classical method (https://arxiv.org/pdf/2306.16028.pdf)
The majority of the project will deal with theory and mathematical connections between complexity and algorithm theory, (quantum) machine learning and theoretical physics, so a strong background in mathematics, theoretical physics or theoretical computer science is advantageous.
We also specially encourage applications from researchers with experience and aptitude in condensed matter and high energy physics (theory or e.g. development of novel numerical and data-driven methods in those fields) who are interested in expanding their research in the direction of quantum algorithms and quantum machine learning.
How to apply
Please send an email with CV and brief statement of interest to the PI Vedran Dunjko
Postdoc in theory & application of quant. machine learning
We offer Postdoc positions as a part of the "Beyond classical quantum machine learning for quantum physical systems" ERC project in the applied Quantum algorithms initiative in Leiden University.
The 5 year project aims to establish new theoretical and practical bridges between quantum machine learning and hard problems in quantum many-body physics, including problems in condensed matter, high energy physics and quantum control.
The research directions (and our recent papers in roughly the same direction for illustration purposes) we aim to explore include:
-development and theoretical and empirical characterization of new classes of quantum machine learning models, and identifying the role of their genuinely quantum features (https://www.nature.com/articles/s41467-023-36159-y)
-development and analysis of novel quantum algorithms for data analysis (e.g. variants of quantum topological data analysis) with provable separations (https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.5.010319))
-characterization of classes of learning problems which can exhibit quantum advantages over any classical method (https://arxiv.org/pdf/2306.16028.pdf)
The majority of the project will deal with theory and mathematical connections between complexity and algorithm theory, (quantum) machine learning and theoretical physics, so a strong background in mathematics, theoretical physics or theoretical computer science is advantageous.
We also specially encourage applications from researchers with experience and aptitude in condensed matter and high energy physics (theory or e.g. development of novel numerical and data-driven methods in those fields) who are interested in expanding their research in the direction of quantum algorithms and quantum machine learning.
