Position OverviewThe
NanoComputing Research Lab in Integrated Circuits (IC) group within the Department of Electrical Engineering of the Eindhoven University of Technology (TU/e) is a leading research group dedicated to pushing the boundaries of knowledge in the field of physic-based computing. We are currently seeking a highly motivated PhD student to join our team to work to focus on the intersection of machine learning and physics to develop concepts for thermodynamic-based computing using coupled oscillators. The ideal candidate will have a strong background in both machine learning and physics, with a keen interest in exploring new analytical and computational paradigms using oscillatory neural networks (ONNs).
ProjectThe successful candidate will be an integral part of the prestigious ERC Consolidator Grant,
THERMODON on harnessing the unique capabilities of ONNs to solve combinatorial optimization problems. ONNs, inspired by the dynamics of coupled oscillators, exhibit inherent properties that enable efficient problem-solving through energy minimization. In this project, we aim to further explore and exploit the potential of ONNs in embedding graph-based problems, particularly those known to be challenging for classical computing architectures.
CandidateWe are seeking a highly motivated junior PostDoc candidate to join our research team in the field thermodynamic-inspired computing combining principles from statistical physics, machine learning and computational techniques. The ideal candidate will have a strong background in statistical physics, non-equilibrium thermodynamics and machine learning.
Knowledge of complex dynamical systems and mathematical methods for characterizing their physical properties constitutes a plus. Standard methods of interest are scaling analyses of networks for their asymptotic properties, characterization through Lyapunov exponents, bifurcation theory, etc.
Duration of PostDoc is 1 year with extension possibility.
Key Responsibilities
- Develop algorithms for ONN thermodynamic-inspired computing combining techniques from statistical physics, machine learning and ONN computing with goal with the goal of designing and implementing these novel algorithms toward an accelerator for efficient thermodynamic ONN simulation on large dynamical data.
- Develop algorithms for ONN thermodynamic-inspired computing while leveraging on the self-organization of ONNs.
- Apply algorithms and benchmark complex problems in combinatorial optimization on different datasets such as scheduling, finance, weather prediction among others.
- Collaborate with a multidisciplinary team to advance the project's aim on developing accelerator hardware based on the ONN thermodynamic-inspired algorithms.
- Mentor and provide support to junior team members.