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PhD in Machine Learning and Physics for Thermodynamic-Inspired Computing

Updated: 05 Mar 2025

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Research / Academic
Eindhoven

Position Overview

The 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 computational paradigms using oscillatory neural networks (ONNs).

Project

The 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.

Candidate

We are seeking a highly motivated PhD 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.

Key Responsibilities

  • Conduct research in developing  computing models based on ONNs and algorithms combining statistical physics and machine learning techniques.
  • Apply concepts from statistical physics, including Markov chains and stochastic processes, to model and implement thermodynamic-inspired neural networks.
  • Develop algorithms for ONN thermodynamic-inspired computing for i) solving combinatorial optimization problems and ii) associative memory applications.
  • Collaborate with a multidisciplinary team to advance the understanding and implementation of novel computing concepts.
  • Communicate research findings and technical information effectively to team members, project and students


Requirements:

  • Bachelor's or Master's degree in Physics, Mathematics, Electrical Engineering,  Computer Science, Artificial Intelligence, Machine Learning,  or a related field.
  • Background in computational physics, with an understanding of how physical principles can be applied to machine learning models and algorithms.
  • Background in statistical physics, Markov chains, and stochastic processes
  • Excellent understanding of neural networks and deep learning
  • Proficiency in Python, C++
  • Experience in Hopfield neural networks and energy-based models.
  • Ideally knowledge of dynamical systems and standard methodologies for characterizing these (e.g. Lyapunov exponents)
  • Familiarity with combinatorial optimization problems and algorithms.
  • Familiarity with AI/ML frameworks (e.g., TensorFlow, PyTorch)
  • Excellent problem-solving skills and a passion for innovative research.
  • Strong communication skills, both written and verbal
  • Ability to collaborate effectively in diverse teams
  • Additional skills: active learning, active listening, critical thinking, growth mindset, planning, organized

Salary Benefits:

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

  • Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
  • Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. €2,901 max. €3,707).
  • A year-end bonus of 8.3% and annual vacation pay of 8%.
  • High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
  • An excellent technical infrastructure, on-campus children's day care and sports facilities.
  • An allowance for commuting, working from home and internet costs.
  • A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates. 
Work Hours:

38 hours per week

Address:

De Rondom 70