Research Projects
Explore available research opportunities
Adaptive Nerve Cuff
This project focuses on designing, fabricating, and testing conductive-polymer-based neural electrodes to enable miniaturized, biocompatible, and energy-efficient closed-loop neuroprosthetic interfaces for next-generation bioelectronic medicine.
Regressing chaos through flow matching towards modelling and design of nonlinear dynamic metamaterials
Develop a machine-learning flow-matching algorithm, capable of predicting behavior of nonlinear dynamic metamaterials, allowing for design of new metamaterial structures.
A comparative assessment of additive manufacturing methods for the production of pure tungsten
A comparative assessment of different additive manufacturing processes will be conducted for pure tungsten material.
Design and implementation of a learning strategy for neuromorphic soft robots
This project develops bio-inspired learning soft robots by integrating an organic neuromorphic “brain” based on organic electrochemical transistors that process and store sensory information in hardware. Coupled to liquid crystal elastomer actuators, the system enables real-time adaptation of robotic behavior through seamless integration of sensing, learning, and actuation.
Numerical Study of Additively Manufactured Tungsten using LPBF for Fusion Applications
A part-scale model for additive manufacturing of tungsten components for nuclear fusion applications will be developed
Automatic identification of “twinning” in metals on the microscale
Develop an automatic method to detect “twinning” in metals using high-resolution deformation (strain) maps and crystal orientation measurements. You’ll work with experimental & synthetic data, building on an existing Matlab slip-identification tool to design, test, and validate a new twinning-recognition approach.
Realize Hardware based Simulated Annealing in a Hardware Based Network
This project explores energy-efficient neuromorphic learning by implementing a hardware-based simulated annealing algorithm that exploits the intrinsic physical properties of memory–processor devices. By realizing a dueling-network learning strategy directly in hardware, the system can autonomously adapt its behavior and converge toward optimal solutions without explicit knowledge of internal weights.
Adaptive Organic Neural Interfacing
This project develops next-generation adaptive neuroprosthetics by using organic electronic devices to create fast, tunable, and minimally invasive neural interfaces on a single chip. By combining organic electronics, materials science, and microfabrication, the work aims to enable personalized electroceutical therapies that sense, adapt, and interact with the nervous system to improve patient outcomes.
Design and manufacturing of 3D morphing scaffolds
Within this project, you will focus on computational design and additive manufacturing through 3D printing of shape-morphing scaffolds that can achieve desired shape change triggered by applications of external magnetic fields for applications in biomedical engineering.
Impact of post heat treatment on performance of 3D printed tungsten products
The impact of post heat treatment will be evaluated on microstructural and mechanical characteristics of 3D printed tungsten Anti scatter grids (ASG).
Characterization of potting material + interfaces in electromagnetic actuators
The microstructure and failure mechanisms of a potting material in electromagnetic actuators are experimentally investigated.
Development of effective constitutive model for polymers actuated by antiferromagnetic nanoplatelets
Develop of a novel constitutive material model that will accurately describe combined mechanical and magnetic behaviour of rubber materials with dispersed antiferromagnetic particles.
Materials meets Machine Learning - Identifying slip systems using neural networks
Identifying crystallographic slip systems in metals is critical to understand how they deform, so we can make them more durable and sustainable. Using machine learning, you will automate this process!
Micromechanical behavior of potting material for electromagnetic actuators
A fundamental understanding of the mechanical characteristics of the heterogeneous potting material in electromagnetic actuators is developed by the development and use of a numerical modelling framework.
Modelling plastic deformation in neutron irradiated aluminum alloys using mean field crystal plasticity
Microstructure evolution due to neutron irradiation of aluminum alloys used for structural components in nuclear research reactors is modelled
Metamaterial-Based Sensing Robot Skin
Design and development of a new class of robotic skins that will allow robots to sense and detect contact.
From experiment to simulation: uncovering the hidden anisotropy of martensite
Experiments show that martensite is anisotropic: its ductility strongly depends on the orientation of the crystal. Can you capture this behavior in a numerical model?
Bending at the microscale - a detailed experimental investigation
You will use high-resolution scanning electron microscopy to observe the complicated plasticity and damage evolution in high-strength steels, thereby contributing to the development of damage-resistant and sustainable steels.
Complex Fluids in Micro- and Nano-fluidic Devices
In this project, you will develop and use finite-element simulations to study polymer solution flows in small-scale channels, accounting for thermal fluctuations and finite-size effects.
Investigating energy absorption of semi-auxetic sandwich composites
Develop autoencoder architecture for inverse design of semi-auxetics optimised for energy absorption. You will first generate training dataset using finite element method, which will be subsequently used generate new geometries with maximum toughness.
Exploring Frequency Propagation on a Neuromorphic Device
This project explores physics-driven learning in neuromorphic hardware by developing a dual-gate, dual-electrolyte device that enables frequency-propagation–based learning within a single physical element. By combining device design, circuit modeling, and network-level demonstrations, the work aims to realize energy-efficient learning and classification directly in hardware without conventional weight updates.
Photo-patternable Solid Electrolytes for Organic Transistor and Neuromorphic Devices
This project aims to unlock more complex organic electrochemical transistor circuits by developing photopatternable solid-state electrolytes based on semi-interpenetrating polymer networks. By enabling fully solid-state OECTs with improved processability and performance, the work advances OMIEC-based bioelectronic and neuromorphic systems beyond the limitations of liquid or gel electrolytes.
Organic artificial Neurons for Robotic Control
This project investigates post-fabrication electrical and biochemical tuning of organic electrochemical transistors to calibrate organic artificial neurons, enabling precise neuromodulation by biological signals or electronic subsystems. By linking in-hardware device characteristics to neuron and control-system behavior, the work aims to integrate organic artificial neurons into adaptive robotic control architectures.
Spiking Neural Network and Artificial Intelligence applications
This project introduces neuromorphic computing by simulating a spiking neural network and quantitatively comparing its performance and energy-efficiency characteristics to a conventional artificial neural network on a selected task.
Predicting Adhesive Capillary Filling in Optical Fiber Array Microstructures
In this project, your work will be focused on the development of a numerical tool to study capillary filling of epoxies in the manufactuing of optical fiber arrays.
Modeling and validation of cuttlefish-inspired fluid diodes
The aim of this project is to develop a numerical model to investigate fluid behavior within a bioinspired fluid diode. To validate the numerical models, a microfluidic experiment will be designed.
Wearable Microfluidic Device for Time-Resolved Sweat Collection in Athletes
This project aims to develop a wearable microfluidic device to efficiently and accurately collect and store time-resolved sweat samples for athlete biomonitoring, enabling post-exercise analysis to provide deeper insight into sweat composition and its physiological information.
Thermo-Mechanical FEM Solver for Powder Bed Fusion
Thermo-mechanical coupling in Powder Bed Fusion drives residual stress and distortion. This project extends a Python FEM thermal solver with a mechanical model to predict stress and deformation. The coupled simulator serves as an RL environment to learn scan strategies and process settings that minimize distortion, benchmarked against classical toolpaths.
Surrogate-Accelerated RL for Toolpath Optimization in Powder Bed Fusion
Powder Bed Fusion scan strategies control temperature history, driving residual stress, distortion, and defects. High-fidelity thermo-mechanical FEM predicts these effects but is too slow for design exploration. This project develops fast surrogate models, using reduced-order or physics-guided machine learning, to rapidly evaluate scan paths and validate optimal strategies with FEM.
Understanding and Improving Interlayer Bonding in 3D-Printed Concrete
Why 3D-printed concrete fails? In this project, you will dive into computational and experimental campaign to understand the link between microstructural features and effective fracture properties of 3D printed concrete.
Investigation of symmetry in mechanical metamaterials
Unraveling relationship between symmetry groups and (meta)material behavior. You will explore and understand the relationship between symmetry and mechanical properties of (meta)materials in relation to buckling.