PhD Position in Experimental Nonlinear Dynamics and Nanomechanics

We are looking for a highly motivated PhD student to join our nonlinear dynamics team. Do you have a broad interest in physics and technology? Do you like the prospect of working in an experimental project in close collaboration with theory? Are you creative, persevering, independent, and a good team player?

You can expect a cutting-edge research project that is very challenging due to the many aspects of science that are involved. For the same reason, it is a highly rewarding experience if you enjoy expanding your knowledge beyond textbook physics. We look forward to meeting you!

Project background

Is it possible to build artificial neural networks from driven nonlinear resonators? This question builds on the ideas of Hopfield and Hinton, but takes them out of equilibrium: in our resonators, a balance of continuous drive and dissipation sustains stable oscillation states. This gives us access to phenomena that are absent in equilibrium physics. For example, this may allow us to investigate how a physical network turns stored memories into dynamical patterns.

In our lab, each node is an electrical circuit or a micro-electromechanical (MEMS) resonator implementing a Kerr parametric oscillator (KPO): a driven, dissipative resonator with two stable oscillation states that act like a bit of memory. We couple several such KPOs, reading out every oscillator simultaneously and in real time. This gives us complete access to the dynamical many-body phase space.

With it, we reconstruct the attractor landscape of the network, track how noise drives switching between collective states, and measure probability currents that appear when detailed balance is broken.

The central question is whether these currents organize into stable, reproducible temporal patterns and coexisting limit cycles. Networks like these bridge nonequilibrium statistical physics and neuromorphic computing, and could provide a physical route to Ising and Boltzmann machines that operate on dynamical rather than static states.

• Build and operate a programmable network of coupled KPOs with up to eight nodes, including control and readout electronics and measurement software.
• Map attractor landscapes, switching statistics and probability currents from measured trajectories.
• Compare experiment with simulations and analytical models in close contact with our theory partners (Prof. O. Zilberberg at the University of Konstanz) and publish and present your results internationally.

• You hold (or are about to complete) an MSc in physics, electrical engineering or a related field, with a solid background in nanomechanics, nonlinear dynamics, or statistical physics.
• You enjoy hands-on experimental work (electronics, signal processing, Python) and are not afraid of a bit of analytical theory.
• Experience with MEMS, lock-in detection, or FPGAs is a plus.
• You communicate well in English.

• A four-year PhD position in the friendly Spin Physics group at ETH Zürich, with an international, stimulating environment
• Technical support from in-house electronics
• FPGA experts
• Close supervision with room for your own ideas
• Employment follows ETH Zürich standards; starting date by agreement

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Working, teaching and research at ETH Zürich

We value diversity and sustainability

In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish.

Sustainability is a core value for us - we are consistently working towards a climate-neutral future.

Curious? So are we.

Further information about Spin Physics can be found on our Website.

Questions regarding the position should be directed to Alexander Eichler, eichlera (no applications).