dr. H. Ruiz (Hans-Christian)

About Me

Given names:  Hans-Christian
Academic titles:  Dr. MSc. (Diplom)
Birthdate and place:  01.08.1984, Mexico City
Present functions:  Assistant Professor of Neuromorphic Computing and AI
 Principal Investigator:  Brain-Inspired Nano-Systems (BRAINS)


Hans-Christian Ruiz Euler is an assistant professor at the Nanoelectronics group and principal investigator of the Center for Brain-Inspired Nano-Systems (BRAINS) at the University of Twente, The Netherlands. H.C. Ruiz Euler has an interdisciplinary background holding a PhD degree in Machine Learning (ML) and a MSc in Theoretical Physics. He has extensive expertise in model inference for stochastic processes, Bayesian time-series analysis and Monte Carlo methods, as well as in deep learning and neural networks.

His research focuses on algorithmic development for unconventional computing systems for efficient physics-based ML and artificial intelligence (AI). In the last years, he developed ML methods for nanomaterial systems resulting in an emerging field that could be referred to as Material Learning. He is first author of the 2020 Nature Nanotechnology paper A deep-learning approach to realizing functionality in nanoelectronic devices. Additionally, H.C. Ruiz Euler pioneered material-based neural networks using high-capacity nanoelectronic devices. His long-term vision is to develop fully autonomous AI systems based on self-adapting, material-based neural networks.


2013-2018 PhD Machine Learning, Radboud University, The Netherlands

2004-2011 Diplom (German MSc) Theoretical Physics, University of Munich (LMU), Germany


2018-2020 Postdoc, Nanoelectronics group, University of Twente, The Netherlands

2020-present Assistant Professor of Neuromorphic Computing and AI, University of Twente, The Netherlands


Engineering & Materials Science
Deep Learning
Deep Neural Networks
Doping (Additives)
Neural Networks
Gradient Descent


The core problem of state-of-the-art deep neural networks is their exponential increase in the number of parameters and arithmetic operations to solve complex tasks. This behaviour can only be mitigated with massive computational resources. To tackle this scalability challenge, Hans-Christian Ruiz Euler investigates the computational properties of unconventional computing systems composed of nodes with high computational capacity and energy efficiency. High computational capacity nodes are tuneable, multi-input, nonlinear activation functions that can solve various linearly inseparable classification tasks. Important examples of high-capacity nodes are biological neurons, which are known to solve XOR, and dopant network processing units (DNPUs) [1], which solve a variety of linearly inseparable binary classification tasks [2,3].

Hans-Christian Ruiz Euler works towards realising scalable brain-inspired hardware based on DNPUs to solve complex machine learning problems in an efficient way. He pioneered material-based neural networks composed of multiple DNPUs that can solve more challenging tasks than single DNPUs [3]. His research involves the algorithmic development of efficient training algorithms for unconventional computing [2,4] and the study of their computational properties [3].


[1] Chen, T. et al, 2020. Classification with a disordered dopant-atom network in silicon. Nature, 577(7790), pp.341-345.

[2] Ruiz Euler, H.C. et al, 2020. A deep-learning approach to realizing functionality in nanoelectronic devices. Nature Nanotechnology, 15(12), pp.992-998.

[3] Ruiz Euler, H.C. et al, 2020. Dopant Network Processing Units: Towards Efficient Neural-network Emulators with High-capacity Nanoelectronic Nodes. arXiv preprint arXiv:2007.12371 (to be published in IOP Neuromorphic Computing and Engineering).

[4] Boon, M.N. et al, 2021. Gradient Descent in Materio. ArXiv preprint 2105.11233.


Other Contributions

Gradient Descent in Materio. Boon, M.N., Ruiz Euler, H.C., Chen, T., van de Ven, B., Alegre-Ibarra, U., Bobbert, P.A. and van der Wiel, W.G., 2021. ArXiv preprint arXiv:2105.11233

Dopant Network Processing Units: Towards Efficient Neural-network Emulators with High-capacity Nanoelectronic Nodes. Ruiz Euler, H.C., Alegre-Ibarra, U., van de Ven, B., Broersma, H., Bobbert, P.A. and van der Wiel, W.G., 2020. IOP Neuromorphic Computing and Engineering

A deep-learning approach to realizing functionality in nanoelectronic devices. Ruiz Euler, H.C., Boon, M.N., Wildeboer, J.T., van de Ven, B., Chen, T., Broersma, H., Bobbert, P.A. and van der Wiel, W.G., 2020. Nature Nanotechnology, 15(12), pp.992-998.

Classification with a disordered dopant-atom network in silicon. Chen, T., van Gelder, J., van de Ven, B., Amitonov, S.V., de Wilde, B., Ruiz Euler, H.C., Broersma, H., Bobbert, P.A., Zwanenburg, F.A. and van der Wiel, W.G., 2020.  Nature, 577(7790), pp.341-345.

Brains-py: python package to facilitate the research on DNPUs. This framework supports data acquisition, deep learning modelling of dopant network processing units, training and research on material-based neural network models.

Particle smoothing for hidden diffusion processes: Adaptive path integral smoother. Ruiz Euler, H.C. and Kappen, H.J., 2017. IEEE Transactions on Signal Processing, 65(12), pp.3191-3203.

Adaptive importance sampling for control and inference. Kappen, H.J. and Ruiz Euler, H.C., 2016. Journal of Statistical Physics, 162(5), pp.1244-1266.

Smoothing estimates of diffusion processes. Ruiz Euler H.C., Kappen H.J., 2016. NeurIPS workshop on Advances in Approximate Bayesian Inference (AABI), Barcelona.

Effective Connectivity from Single Trial fMRI Data by Sampling Biologically Plausible Models. Ruiz Euler, H.C., Kappen, H.J., 2018. ArXiv preprint arXiv:1803.05840

pAPIS: a parallelized importance sampler for time-series based on mpi4py for use with high-performance computing platforms, 2017.

Google Scholar Link

Courses Academic Year  2021/2022

Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.

Courses Academic Year  2020/2021


You can see here a description of our research on dopant network processing units (DNPUs).

If you are looking for a BSc/MSc assignment for your thesis and are interested in the intersection of machine learning (ML) and neuromorphic computing, I have several projects ranging from labwork to ML. Below you can find some examples. All of these projects can be focused on labwork, ML or a combination thereof, so they are suitable for students of CS and Physics. If you find one topic interesting, drop me a line to discuss possible projects.

BSc/MSc Projects:

  1. Device characterization focused on AI functionality (see this paper)
  2. On-chip training via gradient descent (see this paper)
  3. DNPU modelling with ML (see this paper)
  4. DNPU networks for AI (see this paper)

Contact Details

Visiting Address

University of Twente
Drienerlolaan 5
7522 NB Enschede
The Netherlands

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Mailing Address

University of Twente
P.O. Box 217
7500 AE Enschede
The Netherlands