Research Projects
Explore available research opportunities
3D Damage Characterization - What's going on under the steel surface?
Cracks during bending are of critical importance, but using regular microscopy we can only see what's going on at the surface. This experimental project will focus on characterizing damage in 3D using serial sectioning and electron microscopy.
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.
Analyzing the sensitivity of a cluster dynamics model
A cluster dynamics model will be used to study the effect of neutron irradiation on the evolution of the microstructure and the resulting properties of aluminum alloys for structural components of nuclear research reactors.
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.
Layer by layer temprature field 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.
Beat the AI
This project explores rule-based FDM infill strategies using grid models, aiming to outperform AI by reducing moves, material waste, and backtracking on complex geometries printing.
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.
Advancing the Mesh: Enhancing Finite Element Tools for Additive Manufacturing
Enhance a Python tool converting G-code to finite-element meshes by adding belt-printer support, improving performance, and building a GUI for realistic additive manufacturing simulations research.
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
COMSOL vs PyFEM
This Bachelor project compares the open finite-element code PyFEM with the commercial software COMSOL for microstructural materials modelling. The study evaluates modelling flexibility, available material models, numerical performance, usability, and post-processing capabilities using representative homogenization-based case studies.
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.
Investigation of the effective thermal conductivity & thermally induced stresses for potting material in electromagnetic actuators
Thermal conduction and the development of thermomechanical stress in heterogeneous potting material used in electromagnetic actuators is simulated using a micromechanical modelling approach.
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).
In-situ study of shear cutting effects in Advanced Green Steels
Investigate how shear cutting affects the local ductility and damage evolution of green-produced Advanced High-Strength Steels using in-situ tensile testing and optical microscopy.
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!
Using numerical simulations to understand (and predict!) damage in notched steel bending tests
You will employ and optimize a numerical model to understand and predict damage for notched high-strength steel bending specimens.
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.
Mechanical properties of oil paints
In this project, we will apply the T-PAINT method on fragile paint samples that are currently being investigated in delamination studies, allowing us to support and clarify the mechanical failure mechanisms of the paint layers.
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.
Optimizing Surface Treatments for Electron Microscopy of Green Steel
You will systematically assess and benchmark surface treatment strategies to ensure reliable SEM characterization of nanometer-scale inclusions in advanced high strength steels.
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.
Integrated Sweat Volume and Rate Sensor for Wearable Biomonitoring of Athletes
This project aims to develop a next-generation microfluidic wearable for continuous sweat monitoring by integrating sweat volume and rate sensing. Using passive pumping and electrodes, the system enables accurate, real-time sweat-rate measurement to improve athlete biomarker analysis.
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.
MAC plate: fabrication of an innovative microfluidic cell culture platform with flow
ARTIC Technologies, a spin-off of TU/e, develops novel platforms to create flow for a better drug development. You will be part of our professional team and help us fabricate the platforms for our customers.
Effects of artificial cilia shape on fluid flow rates
Magnetic Artificial Cilia (MAC) are small, magnetic cylinders nowadays used to pump fluid through microfluidic channels. You will test the effect of various cross-sectional shapes of MAC to enhance fluid flow rate.
Non-invasive kidney function monitoring via sweat sensing
DXcrete is a spin-off company from the Microsystems section, developing a wearable sweat sensing device which enables non-invasive monitoring of patients. The aim of this project is to evaluate and select suitable creatinine sensors for integration into the device.
Modeling of lap shear experiments for potting material used in electromagnetic actuators
Lap shear experiments are modelled in order to obtain insights in the local stress state in the potting material of electromagnetic actuators under applied load.
Probing viscoelastic flow with deformable particles: a numerical study in cross-slot geometries
The goal of this project is to numerically study how an elastic particle (modeled as a Neo-Hookean solid) deforms in a viscoelastic fluid in a cross-slot geometry.
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.
Computational Investigation of the Effect of Scanning Strategies on the Thermal Field in Laser Powder Bed Fusion of Tungsten for Nuclear Fusion Applications
A single layer finite element–based thermal model is developed and used to study the effect of different scanning strategies on the thermal field for laser powder bed fusion of tungsten.