Control Theory

Seminar by Morten Kjaergaard (Niels Bohr Institute, University of Copenhagen) on rapid FPGA-based feedback for superconducting qubits, including sparse-sampling techniques and on-FPGA inference enabling millisecond-timescale T1 estimation, ~100 ms readout optimization, and over 74,000 consecutive recalibrations in closed-loop operation.

Stephanie Gil studies reliable coordination in robot networks facing malicious information and ordinary environmental uncertainty. Communication signals provide physical evidence of trustworthiness that is difficult to forge; the cy-trust framework turns that evidence into probabilistic trust estimates and weights neighboring agents accordingly. For consensus and distributed optimization, the analysis establishes almost-sure convergence with bounded departures from nominal performance even when malicious agents form a majority of a node’s neighbors, exceeding the classical Byzantine threshold. Theory and hardware experiments support these guarantees. For natural uncertainty, real-time sensing is incorporated into rollout-based reinforcement learning, reweighting possible futures. Applications include fleet routing under random demand and Project CETI’s autonomous robotic rendezvous with sperm whales at sea. The talk closes with directions for combining trust and long-horizon sequential decisions to retain resilience when planning data may be corrupted.

The flagship conference of the IEEE Robotics and Automation Society, held May 24-28, 2027 at Coex in Seoul, South Korea, bringing together the world's top researchers and industry leaders to advance the field of robotics.

The traditional view of a neuron as a feature detector or an efficient encoder has difficulties in explaining the function of motor neurons and experimentally observed variable and context-dependent response properties of neurons. We put forward an alternative perspective, modeling each neuron as a feedback controller within a closed loop that includes other neurons and the external environment. Based on the recently developed Direct Data-Driven Control (DD-DC) approach, we propose a biologically plausible controller which implicitly identifies the dynamics of the rest of the loop, infers its latent state and optimizes control. The DD-DC model of a neuron accounts for multiple neurophysiological observations, including the switch from potentiation to depression in Spike-Timing-Dependent Plasticity (STDP) and its asymmetry; temporally extended feedforward and feedback neuronal filters and their adaptation to input statistics; imprecision of the neuronal spike-generation mechanism under constant input; as well as the prevalence of variability and/or noise in brain operation. The DD-DC neuron offers an alternative to the feedforward, instantaneously responding McCulloch-Pitts-Rosenblatt unit as a primitive for constructing biologically-inspired neural networks. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2023-06-28. Recording duration: 00:52:20.

A 23-month postdoctoral appointment at 0.8 FTE in Pieter Medendorp’s sensorimotor research group at the Donders Institute. The project investigates perception, movement and decisions under uncertainty through behavioural experiments and computational models. Work combines multisensory integration, Bayesian modelling and optimal control with virtual reality, wearable sensors and motion platforms, including natural navigation, orienting and reaching. The preferred start is 1 December 2026. Responsibilities may include student supervision and teaching.

The Control and Automation group at inspire AG, an ETH Zurich strategic partner, offers a full-time postdoctoral position in Zurich with an industrial partner and ETH’s Automatic Control Laboratory. The researcher will develop iterative learning control and parameter-optimisation algorithms, connect theoretical analysis to tests on industrial hardware, and design adaptive control and estimation methods. The project aims to improve real-world industrial efficiency through learning-based control, process monitoring and practical guarantees. The advertised planned start is October 2026; the source labels the appointment permanent and specifies no application deadline. Applications are currently accepted according to the official vacancy instructions. Applicants send the listed documents to the employer with reference “ILC Postdoc”; the source page supplies the contact and document requirements.

Seminar by Morten Kjaergaard (Niels Bohr Institute, University of Copenhagen) on rapid FPGA-based feedback for superconducting qubits, including sparse-sampling techniques and on-FPGA inference enabling millisecond-timescale T1 estimation, ~100 ms readout optimization, and over 74,000 consecutive recalibrations in closed-loop operation.

A 23-month postdoctoral appointment at 0.8 FTE in Pieter Medendorp’s sensorimotor research group at the Donders Institute. The project investigates perception, movement and decisions under uncertainty through behavioural experiments and computational models. Work combines multisensory integration, Bayesian modelling and optimal control with virtual reality, wearable sensors and motion platforms, including natural navigation, orienting and reaching. The preferred start is 1 December 2026. Responsibilities may include student supervision and teaching.

Stephanie Gil studies reliable coordination in robot networks facing malicious information and ordinary environmental uncertainty. Communication signals provide physical evidence of trustworthiness that is difficult to forge; the cy-trust framework turns that evidence into probabilistic trust estimates and weights neighboring agents accordingly. For consensus and distributed optimization, the analysis establishes almost-sure convergence with bounded departures from nominal performance even when malicious agents form a majority of a node’s neighbors, exceeding the classical Byzantine threshold. Theory and hardware experiments support these guarantees. For natural uncertainty, real-time sensing is incorporated into rollout-based reinforcement learning, reweighting possible futures. Applications include fleet routing under random demand and Project CETI’s autonomous robotic rendezvous with sperm whales at sea. The talk closes with directions for combining trust and long-horizon sequential decisions to retain resilience when planning data may be corrupted.