Master’s Thesis Position in Pediatric Cancer Metabolism
Join the Morscher Lab at the University Children’s Hospital Zurich and contribute to cutting-edge research in pediatric cancer metabolism. We have an exciting Master's Thesis position available for a motivated student interested in biochemistry, metabolism, and cancer research.
Establishing a New Method for Measuring Mechanical Properties of Cells and Organoids with Acoustofluidics
Are you an engineer or material scientist who would like to help understanding biology and cell behavior? Are you fascinated by processes on the micron-scale and like to build and try out things? Then this might be your project!
Microfluidic Automation for Personalized Cancer Treatment
Do you want to play with microfluidics, automation, cells, and microscopy, AND help to improve cancer treatments? Then this might be the right student project or internship for you! (Technical interest is necessary, but also non-engineering students can apply.)
PhD position in neuroimaging of lower urinary tract function
The Department of Neuro-Urology at the Spinal Cord Injury Center, University of Zürich and Balgrist University Hospital in Zürich, Switzerland, is offering a PhD positions for a highly motivated and scientifically interested PhD candidate to conduct clinical research joining neuroimaging with neuro-urology in humans. This position is embedded in an interdisciplinary research environment team of health care professionals, neuroscientists, physicists, biologists, human movement scientists, pharmacologists, and engineers. You will be involved in a nation-wide randomized, sham-controlled, double-blind clinical trial investigating transcutaneous tibial nerve stimulation in patients with acute spinal cord injury to prevent neurogenic detrusor overactivity (http://p3.snf.ch/Project-179644, supported by the Swiss National Science Foundation (SNSF) and the Swiss Paraplegics Foundation (SPS).
Using a sensor of cellular age to track neural stem cell aging
We have developed an age-sensor that translates cellular age into measurable fluorescence. The aim of this project is to use this age-sensor to track how neural stem cells age over time when overexpressing toxic proteins.
Lab help (long-term) 20-25h/month
We are looking for a motivated bachelor level undergraduate who wishes to earn a bit of money by helping with routine lab maintenance tasks. This includes autoclaving, recycling, restocking, making bacterial plates and more.
3D Multi-View Surface Reconstruction Using RGBD Data
The goal of this project is to implement and evaluate state-of-the-art 3D surface reconstruction methods using multi-modality sensor data.
The impact of placebo on autonomic responses
The autonomic nervous system reacts to pain, for example by adapting the heart rate or increased sweat output at the hands. Such pain-autonomic responses have been shown to be increased in chronic pain patients with a sensitized nociceptive system (Scheuren et al., 2023). However, pain-autonomic responses can also be influenced be positive and negative expectations (Aslaksen et al., 2008; Rhudy et al., 2018). Therefore, we aim to disentangle the effects of expectations and sensitization on pain-autonomic responses. For this reason, we will conduct a study in healthy participants, using experimentally induced sensitization and a placebo paradigm.
Quantitative assessment of atherosclerosis in patients with multispectral optoacoustic tomography
Optoacoustic (OA, also known as photoacoustic) imaging has massively evolved since the early 2000s when the in vivo feasibility was first demonstrated. This new imaging modality combines the advantages of optics and ultrasound to provide high-resolution images of spectrally-distinctive absorbers deep into tissues. This can be exploited e.g. to visualize oxygenated and deoxygenated hemoglobin as well as lipids or other chromophores with high specificity. After more than 15 years of growing use in preclinical research, OA is now being considered as a clinical imaging modality, with initial clinical trials offering promising results. In this context, we are looking for a motivated master student that can have the opportunity to analyze clinical data acquired with a state-of-the art OA imaging system.
Empa-ETH Collaboration: Multiplexed Aptamer Patterning on Hydrogels
Our goal is to pattern different DNA-based recognition elements or aptamers, precisely inside hydrogels that can serve as 3D microenvironments for tissue engineering, disease modeling, and drug screening. By integrating aptamers into hydrogel networks, we will create an interactive environment where molecules are scavenged or released inside hydrogel scaffolds.
Soft robotics for single-pixel ultrasound imaging
Take part in an cutting-edge project aimed at using soft-robotics methods to change the shape of a single ultrasound transducer to form an image. Develop a prototype, model its ultrasound response and test it in our labs.
Characterization and investigating 2D skin disease model via biosensing and optical imaging
Pemphigus vulgaris (PV) is a unique group of autoimmune diseases. Researches have demonstrated that antibody-induced disruption of Dsg3 transadhesion initiates a signaling response in basal keratinocytes followed by loss of tissue integrity. The complexity of morphogenesis and tissue regeneration implies the existence of a transcellular communication network in which individual cells sense the environment and coordinate their biological activity in time and space. To understand the fascinating ability of tissue self-organization, comprehensive study of biophysical properties (cell topography and bioelectricity) in combination with the analysis of biochemical networks (signaling pathways and genetic circuits) is required. Together with the University of Bern and University of Lübeck, we aim to utilize the tools to study the topography and electrophysiology (cell potential, ion channel recording, localized ion detection, charges) of HPEK cells (human primary keratinocytes cells) to unravel the signaling pathways of the disease. We utilize optical imaging (fluorescence dyes) and biosensing tools (including the state of the art hs-SICM and electrical FluidFM setup) to study HPEK cells upon desmosome disruption.
Master Project Opportunities in Prostate Cancer Research
Our lab offers several master projects. Interested students can choose between projects associated with (1) Therapy and Biomarkers in Prostate Cancer (2) Investigating the DNA repair mechanism in prostate cancer. 3)investigating the role of extracellular vesicles in prostate cancer metastasis.
Advancing Spinal Fusion Surgery Predictions
Join us in this exciting project that seeks to contribute to the improvement of spinal fusion surgery. This project offers the chance to develop and validate an advanced pipeline based on finite element modeling, other mechanical modeling approaches and computer vision. By leveraging a comprehensive dataset of pre- and postoperative CT scans, you'll have the opportunity to closely collaborate with clinicians and research engineers, ensuring the real-world applicability of your work.
Master student project: Generative AI model for spine 3D images generation
The proposed master thesis project aims at exploring different libraries of generative AI models (Khader et al., 2023; Tudosiu et al., 2022; Zhou et al., 2019, 2021) for the creation of spine synthetic datasets. These synthetic datasets will evaluated by medical professionals and then used for segmentation and biomechanical modelling (Caprara et al., 2021).
Generative AI model for spine 3D images generation
The proposed master thesis project aims at exploring different libraries of generative AI models (Khader et al., 2023; Tudosiu et al., 2022; Zhou et al., 2019, 2021) for the creation of spine synthetic datasets. These synthetic datasets will evaluated by medical professionals and then used for segmentation and biomechanical modelling (Caprara et al., 2021).
Automatic bone, nerve and muscle segmentation from MR imaging of the lumbar spine
The current master thesis project aims at using currently open-source deep learning pipelines for image segmentation such as nnUNet and/or SAM (Isensee et al., 2021; Kirillov et al., 2023) for automatic segmentation of bone and soft tissue from magnetic resonance imaging (MRI) of the lumbar spine.
Eradicating the Unseen Threat: Developing the First Diagnostic Test for Flesh-Eating Bacteria
Are you ready to channel your passion for science into a project that profoundly impacts human lives? Join us in the groundbreaking initiative to develop the first-ever diagnostic test for detecting flesh-eating bacteria, Mycobacterium ulcerans, the pathogen behind Buruli Ulcer disease. This tropical disease inflicts severe suffering and lasting disabilities, particularly affecting children in West Africa. If you're a highly motivated student seeking real-world impact, this interdisciplinary and fast-paced venture is tailor for you. Prior experience is beneficial but not necessary, as our expert guidance and cutting-edge technology will empower you to succeed.
Pioneering Point-of-Care Solutions: Co-Developing a Novel Diagnostic Device for At-Home Blood Testing
Are you a passionate and motivated student eager to make a real impact in healthcare? Our exciting opportunity perfectly aligns with your drive to learn and contribute. Our groundbreaking project involves co-developing a novel diagnostic device for at-home blood testing, and we're looking for individuals like you to join our dynamic team. In this project, you will build and optimize a microfluidics-based rapid antigen test that integrates with our state-of-the-art electrochemical readout technology. This innovation will pave the way for a revolutionary point-of-need diagnostics device. Guided by our expert supervision, you'll be at the forefront of assay optimization, focusing on critical aspects such as reducing non-specific binding, enhancing sensitivity, and refining assay reproducibility.
Vision-based State Estimation for Flying Cars
Vision-based State Estimation for Flying Cars
Acoustic radiation force simulation for spherically focused array.
Take part in an cutting-edge project aimed at understanding how ultrasound can modulate the activity of neurons. Develop a model to characterize the radiation force generated by our top-notch ultrasound neuromodulation system.
Next-generation thin metal aptamer-based biosensor
This project aims to test and characterize a novel aptamer-based biosensor that can detect small molecules. The project lies at the intersection of engineering, chemistry, and materials science.
Revolutionizing high spatio-temporal resolution imaging of cerebral blood flow
Cerebral blood flow (CBF) imaging with high spatio-temporal resolution holds immense significance, given its direct correlation with physiological status and its potential to indicate neural activity changes or pathological conditions. However, this pursuit has been hindered by a lack of effective neuroimaging tools. Recently, we proposed a widefield fluorescence localization microscopy (WFLM) approach that can achieve capillary level spatial resolution across the entire mouse cortex [1,2]. However, the temporal resolution lags, and the requisite high-speed, large pixel resolution camera proves elusive, hindering the applicability of this technique.
Master thesis: Neuroimaging (EEG and/or MRI) to assess lower urinary tract (dis-)function in patients with neurological diseases
We are looking for highly motivated and scientifically interested master candidates to conduct clinical research joining neurophysiology and neuroimaging with neuro-urology in humans. This post is embedded in the research environment of the Department of Neuro-Urology and the Spinal Cord Injury Center, University of Zürich, and Balgrist University Hospital in Zürich, where you will be working in an interdisciplinary team of health care professionals, neuroscientists, physicists, biologists, human movement scientists, pharmacologists, and electrical engineers.
Master thesis: Neurocomputational phenotyping of psychosis
This study aims to subclassify schizophrenia patients based on brain activity patterns during decision making. This would not only advance our limited biological understanding of schizophrenia but also directly enable physicians to apply the right medication from the beginning instead of trial-and-error treatment over months.
Development Of An FPGA-Based Optoacoustic Image Reconstruction Platform for Clinical Applications
Optoacoustic (OA) imaging is a hybrid imaging method that enables deep tissue imaging with a high spatial resolution by combining optical illumination with ultrasound detection. The goal of this student project is to devise a parallel HW accelerator and explore different HLS code optimizations to achieve the best performance for OA image reconstruction on an FPGA in real-time.
Revolutionize at-home diagnostics
Join our interdisciplinary student project to transform at-home diagnostics! Work on cutting-edge technology, boost sensitivity, engineer tests for seamless home use, and develop targeted disease detection. Help us to shape the future of healthcare.
Investigating cells mechanical properties via Fluidic Force Microscopy in a 2D in-vitro autoimmune skin disease model
The remarkable complexity of morphogenesis and tissue regeneration implies the existence of a transcellular communication network in which individual cells sense the environment and coordinate their biological activity in time and space. To understand the fascinating ability of tissue self-organization, comprehensive study of biophysical properties (cellular nanomechanics such as tension forces and bioelectromagnetics) in combination with the analysis of biochemical networks (signaling pathways and genetic circuits) is required. In this framework we are investigating the unacknowledged key role of Desmoglein 3 (Dsg3) as a receptor involved in mechanosensing, capable of initiating a signaling response in the transcellular communication network, which results in stem cell fate conversion, plasticity and tissue repair. Our goal is to apply innovative Fluidic Force Microscopy to measure altered biophysical parameters upon disruption of Dsg3 transadhesion such as cell stiffness, cell-cell adhesion, cell surface charges and electric potentials. Together with the University of Bern and University of Lübeck we are further investigating how these biophysical changes relate to transcriptomic, epigenomic and proteomic response circuits to ultimately infer biophysical and biochemical circuits involved in Dsg3 signaling.
Exploring Multimodal Strategies for Event-Based Vision
This project focuses on utilizing multi-purpose vision models in the realm of Event-Based Vision.
Enhancing Event Data Processing with Irregular Time Series Modeling
This project focuses on utilizing an advanced approach to time series modeling for efficient event data processing.
Engineering porous synthetic hydrogels for vascularization and drug screening applications
Porous hydrogel materials have been shown to hold several advantages over non-porous materials in recent years. We have collected promising preliminary data on the creation of porous hydrogel matrices based on enzymatically cross-linked poly(ethylene glycol) (PEG). We are currently further optimizing and characterizing these materials. In parallel, our lab has developed several vascularization models based on nanoporous PEG. We have used them to answer basic biological questions and developed specialized assays for anti-cancer drug screening applications. We would now like to use the newly characterized porous material to create vascularized niches. We are looking for a highly motivated master thesis student to bridge these two projects and tackle technical challenges, ask and answer interesting biological questions.
Single cell multiplexed imaging of drug resistance networks in ovarian cancer (ETH/UZH)
Ovarian cancer is the most lethal gynecological cancer with chemoresistance contributing to high rates of relapse and mortality. This project aims to investigate and predict drug response in over 100 ovarian cancer patients using multiplexed imaging. The Bodenmiller Lab employs highly multiplexed technologies including spatial transcriptomics and imaging mass cytometry. In this project both technologies are combined in conjunction with ex vivo studies to model drug response in ovarian cancer.
Generative AI for synthesizing realistic MRI of the developing human brain
The project aims to develop and enhance a novel generative AI-based method for synthesizing MRI images from label map images representing different anatomical brain tissues. Previous works have shown the feasibility of GAN-based methods, but this project focuses on applying stable diffusion networks to synthesize realistic MRI images of developing human brains. The research will also involve evaluating the performance of the proposed method against other approaches, such as GANs and MRI physics modeling-based synthesis, and the resulting code will be made available on GitHub. The project offers an opportunity to contribute to cutting-edge research in the field of fetal and infant brain imaging. Please note that for patient data confidentiality reasons, we CANNOT ACCEPT CANDIDATES OUTSIDE the UZH/ETH domain or outside the Swiss institutions we collaborate with.
Host opportunity - Swiss Postdoctoral Fellowships, funded by the Swiss National Science Foundation
The Fetal and Infant Imaging group, led by Prof. Andras Jakab, invites motivated international post-docs to apply for the Swiss Postdoctoral Fellowship at the University of Zürich / University Children's Hospital Zürich. Close collaboration between the postdoc and the PI from our host institution is emphasized during the application process to ensure a well-aligned research topic. This opportunity offers access to exciting clinical datasets, cutting-edge neuroimage analysis methods, and fruitful collaborations both locally and internationally. Additionally, the city of Zürich provides an excellent quality of life, and successful applicants will receive a gross annual salary of approximately $115,000 (CHF 100,000) along with a dedicated project budget for running costs and experiments. If you are passionate about advancing research in human neurodevelopment and neuroimaging, apply now and take your career to the next level with us.
Assay developement for cancer diagnostics
You will develop a diagnostic test for testicular cancer. The focus of the project will be on creating the biochemical protocols for the test. The project is in collaboration with a prelaunch startup and a hospital (USZ). Therefore, it is ideal for motivated students who want to have a direct impact
Develop the electronics for a new medical sensor
You will work on bringing medical tests to peoples home. You will further develop the hardware and software for a readout device that can perform a variety of diagnostic tests in a reliable but simple fashion.
Simulation of Cardiovascular Magnetic Resonance Imaging
The aim of this project is to extend our existing MR simulation framework, CMRsim, with additional MR physics or particle tracking related effects.
FluidFM-based nanoscale printing for wearable self-powering biomedical real time sensing
What about implantable self-powering devices to monitor biophysical signals at nanoscale? As a part of the interdisciplinary frontier between material science and new biomedical applications, being able to monitor biological or physical markers and signals, allows for a better treatment from both the diagnostic and healing point of view. Among them, biocompatible and non-intrusive wearable monitoring devices, which are so flexible to adhere perfectly to biological tissue, and even to cells like neurons, gain increasing interest. However, fabricating the devices and the electrodes at nano/microscale remains a challenge. FluidFM is a force-controlled nanopipette, a versatile tool also for 2D patterning and 3D printing in liquid environment, opening the opportunity to manufacture the devices at the sub-micron scale. We are going to create the devices and electrodes depositing conductive polymers with the FluidFM and then to perform the opportune electrical characterization.
Semester student
Our lab is looking for a highly motivated semester student, who is interested to conduct research in the field of regenerative therapies after traumatic brain injuries. The process will involve learning how to work in translational research in an in vivo model of mild diffuse brain injury in mice, how to conduct and analyse behavioral assessments, as well as how to scientifically interpret and discuss results.
Patient*innenperspektiven zur Akzeptanz und Nutzbarkeit digitaler Gesundheitstechnologien in der Onkologie - eine qualitative Studie
Digitale Werkzeuge wie Conversational Agents und Wearables bieten vielversprechende Möglichkeiten zur Erfassung von Patient-Reported Outcomes (PROs) und objektiven Krankheitsverläufen, wodurch das Wohlbefinden der Patientinnen verbessert werden kann und eine personalisierte Behandlung in der Onkologie ermöglicht wird. Die erfolgreiche Einführung und Umsetzung dieser Werkzeuge in herkömmlichen Gesundheitssystemen kann jedoch Herausforderungen mit sich bringen. Schlüsselfaktoren sind die Einbindung der Patientinnen, die Akzeptanz und die Anwendungsfreundlichkeit des Systems. Um diese entscheidenden Aspekte anzugehen, ist ein nutzungsorientrierter Ansatz unerlässlich, der sich darauf konzentriert, die Bedürfnisse der Patient*innen zu verstehen und technologiebedingte Barrieren zu überwinden.
Machine Learning-Based Automated Analysis of Murine Brain Corrosion Casts
This project aims to investigate the use of machine learning-based algorithms to obtain a deeper understanding of the graph-structured vasculature preserved in corrosion casts. For a more detailed description, please refer to the attached document.
Advancing Single-Molecule Sensing for Protein Sequencing
In this project, you will have the opportunity to contribute to the development and optimization of a single-molecule sensor designed for the detection, identification, and sequencing of important biomolecules such as DNA and proteins. The sensor technology is built upon the principles of microfluidics, nanofabrication, and machine-learning data analysis. It is an excellent fit for students who possess skills and a strong interest in these fields and are eager to engage in an interdisciplinary project with significant potential impact.
Assembling an advanced high-speed SICM for live cell imaging
Scanning ion conductance microscopy (SICM) is the non-​contact SPM technology to image live cells based on glass capillaries with a nanometric aperture. It applies a voltage and measures the ionic current flowing through the pipette above the sample in the buffer solution: the recorded current represents the feedback signal to measure the topography of the sample. In collaboration with Prof. Fantner at EPFL, this project aims to assemble a state of the art high-​speed SICM to enable time-​resolved live cell imaging.
Debris of a feast: Analyzing leftovers after burst of pathogenic bacteria due to phages or predatory bacteria
Antibiotic resistance is one of the biggest threats to public health and alternatives to conventional antibiotics are urgently needed. An innovative way to kill pathogenic bacteria is to use their natural enemies bacteriophages or a periplasmic predatory bacterium (Bdellovibrio bacteriovorus). While the detrimental destruction of pathogenic bacteria can be an advantage to stimulate the immune response of the infected eukaryote, too much immune stimulation may lead to toxic shock. This is a Master thesis opportunity at University of Zurich, Irchel campus.
Non-contact pulse oximetry for MRI
Pulse oximetry during Magnetic Resonance Imaging (MRI) helps to monitor the well-being of the patients and to obtain information about their physiology. However, the standard oximetry finger clip complicates the process and therefore it is the aim of the project to build a contactless pulse oximeter that can be integrated into a novel low-field MRI scanner. To this end, an MRI compatible camera will be deployed to illuminate the face and to quantify the reflected light intensity on volunteers. Machine learning algorithms will be used to derive physiology triggers to e.g. control the MRI data acquisition process.
Implementation of Image Registration Toolbox
The project aims to modernize and improve the process of medical image registration, currently performed through a method known as pTV. Offering a unique combination of numerical programming and practical software implementation, this project promises visibility and application in the ever-evolving field of medical imaging technology. Suitable as a semester-long or master's project.
Bayesian Optimization for Racing Aerial Vehicle MPC Tuning
In recent years, model predictive control, one of the most popular methods for controlling constrained systems, has benefitted from the advancements of learning methods. Many applications showed the potential of the cross fertilization between the two fields, i.e., autonomous drone racing, autonomous car racing, etc. Most of the research efforts have been dedicated to learn and improve the model dynamics, however, the controller tuning, which has a crucial importance, have not been studied much.
Drone to Drone Interaction Effects
Use aerodynamic effects to infer the other drones’ locations.
End-to-End Vision-Based Landing
Land a UAV safely relying on vision.
Localization techniques for drone racing
Benchmark comparison of localization techniques.
Computational Photography and Videography
Combine the complementary information from standard and event cameras to enhance images and video.
Deep learning based motion estimation from events
Optical flow estimation is the mainstay of dynamic scene understanding in robotics and computer vision. It finds application in SLAM, dynamic obstacle detection, computational photography, and beyond. However, extracting the optical flow from frames is hard due to the discrete nature of frame-based acquisition. Instead, events from an event camera indirectly provide information about optical flow in continuous time. Hence, the intuition is that event cameras are the ideal sensors for optical flow estimation. In this project, you will dig deep into optical flow estimation from events. We will make use of recent innovations in neural network architectures and insights of event camera models to push the state-of-the-art in the field
3D reconstruction with event cameras
This project will explore the application of event camera setups for scene reconstruction. Accurate and efficient reconstructions using event-camera setups is still an unexplored topic. This project will focus on solving the problem of 3D reconstruction using active perception with event cameras​.
Event-based depth estimation​
This project will focus on event-based depth estimation using structured light systems.
Learning to calibrate an event camera
This work will address intrinsic calibration of event cameras, a fundamental problem for application of event cameras to many computer vision tasks, by incorporating deep learning.
Model-based Reinforcement Learning for Autonomous Drone Racing
Recent advances in model-free Reinforcement Learning have shown superior performance in different complex tasks, such as the game of chess, quadrupedal locomotion, or even drone racing. Given a reward function, Reinforcement Learning is able to find the optimal policy through trial-and-error in simulation, that can directly be deployed in the real-world. In our lab, we have been able to outrace professional human pilots using model-free Reinforcement Learning trained solely in simulation.
Development of Off-Board Vision System Software for Autonomous Drones
This project aims to develop the software for an off-board vision system for an autonomous drone. The objective is to enhance the capabilities of the existing drone hardware by integrating a real-time image transmission system. The software will enable the drone camera to transmit high-quality images and videos in real-time to a remote receiver.
Data-driven Event Generation from Images
In this project, the student applies concepts from current advances in image generation to create artificial events from standard frames. Multiple state-of-the-art deep learning methods will be explored in the scope of this project.
Data-driven Keypoint Extractor for Event Data
The project aims to develop a data-driven keypoint extractor, which computes interest points for event camera data. Based on a previous student project (submitted to CVPR23), the approach will leverage neural network architectures to extract and describe keypoints in an event stream.
Domain Transfer between Events and Frames
In this project, the student extends upon a previous student project (published at ECCV22) and current advances from the UDA literature in order to transfer multiple tasks from frames to events. The approach should be validated on several tasks in challenging environments (night, high-dynamic scenes) to highlight the benefits of event cameras.
Deep-learning based automatic localization and tracking of mitral valve annulus from cardiac magnetic resonange images
This project aims at developing a machine learning approach (for example, using convolutional neural networks) for localizing and tracking mitral valve annulus from cardiac MR images.
Deep learning for synthetic scar generation
This project focuses on the development of deep learning methods for the synthesis and analysis of cardiac LGE images.
Clincial Validation of Self-supervised Algorithms for Total Body Screening
Department of Dermatology at the University Hospital Zurich is looking for Bachelor or Master students for clinical validation of self-supervised algorithms for ugly duckling characterization.
Next-Generation DNA-Based Biomedical Sensors
Our goal is to develop paradigm-shifting electronic biosensors to detect neurochemicals released in the brain and biomarkers in clinically relevant human fluids. These novel biosensors will help us answer relevant neuroscience questions and tackle human health concerns.
In-silico cardiac and cardiovascular modelling with physics informed neural networks
The aim of the project is to investigate the benefits, requirements and drawbacks of physics informed neural networks in the context of personalised cardiac and cardiovascular models
Deep-learning based generation of synthetic cardiac phantoms for healthy and pathological anatomy and function
The project focuses on the development of a synthetic numerical phantom for cardiac anatomy and function suitable for representing population variability.
Investigating synaptogenesis in iPSC-derived neurons from ADHD patients after Methylphenidate treatment
Our lab is looking for a highly motivated Bachelor/Master student, who is interested to conduct a research in the field of disease modeling using ADHD patient-specific cells. The process will involve learning how to generate and culture human induced pluripotent stem cells (iPSCs) and generation of neural stem cells (NSCs) from patients with Attention-Deficit Hyperactivity Disorder (ADHD), as well as how to scientifically interpret and discuss scientific papers and conduct research with independence and critical thinking.
Investigation of the role of the Wnt signaling in the proliferation of ADHD-derived neural stem cells after Methylphenidate treatment
Our lab is looking for a highly motivated Bachelor/Master student, who is interested to conduct a research in the field of disease modeling using ADHD patient-specific cells. The process will involve learning how to generate and culture human induced pluripotent stem cells (iPSCs) and generation of neural stem cells (NSCs) from patients with Attention-Deficit Hyperactivity Disorder (ADHD), as well as how to scientifically interpret and discuss scientific papers and conduct research with independence and critical thinking.
Proteomic investigation of BDNF and Wnt-Signalling after Methylphenidate and/or Omega-3 treatment in human iPSC-derived neural stem cells from ADHD patients
Our lab is looking for a highly motivated Master student (6-12 months), who is interested to conduct a research in the field of disease modeling using ADHD patient-specific cells. The process will involve learning how to generate and culture human induced pluripotent stem cells (iPSCs) and generation of neural stem cells (NSCs) from patients with Attention-Deficit Hyperactivity Disorder (ADHD), as well as how to scientifically interpret and discuss scientific papers and conduct research with independence and critical thinking.
Efficient Learning-aided Visual Inertial Odometry
Online learning-aided visual inertial odometry for robust state estimation
Improving Robotic Spinal Surgery Precision and Safety with Advanced Imaging Techniques
This project aims to improve the precision and safety of spinal surgeries by developing a new method for placing screws in the vertebrae. The current method uses an optical navigation system that helps surgeons visualize the patient's spine in real-time during the surgery, but there can be errors due to patient movement. To address this, a student will use advanced computer technologies like deep learning to segment the vertebrae from imaging data, allowing the preoperative image to be registered to the segmented vertebrae and eliminating any errors caused by patient movement. The developed method will be evaluated in future experiments to ensure it can improve the safety and accuracy of pedicle screw placement in spinal surgeries.
Developing Smart Vision Assistive Technology
Developing Smart Vision Assistive Technology
PhD or Postdoctoral Researcher in Bio-inspired, Model-Based Reinforcement Learning
The Grewe lab (www.grewelab.org) at the Institute of Neuroinformatics (www.ini.uzh.ch/en.html) and the ETH AI Center (ai.ethz.ch) at ETH Zurich invites applications for a Postdoctoral position in the area of bio-inspired, model-based reinforcement learning. We are particularly interested in candidates who can contribute to our ongoing research endeavors to understand and model the complex and dynamic patterns of neuronal activity inspired by how the brain operates and solves tasks.
Adversarial Robustness in Event-Based Neural Networks
The project will focus on studying various neural network architectures for event-based inference datasets and evaluate their performance in the presence of adversarial attacks.
Development of Machine Learning Methods for Radiation Protection and Medical Imaging
The number of interventional procedures has been increasing because of the numerous benefits for the patient, the significant technological development and the number of operators performing procedures outside the traditional radiology department. However, exposure to ionizing radiation may have detrimental health effects to both operators and patients. Longer and more complex fluoroscopic procedures are associated with tissue reactions such as skin erythema, epilation or cataract. The radiation exposure depends on clinical factors (patient size, number of stents, tortuosity of blood vessels, etc.) as well as geometrical factors such as the image projection and table position. Machine learning methods can be used to determine which factors contribute to the radiation exposure in order to provide valuable advice to the operators to optimize the procedure.
Neural-based scene reconstruction and synthesis using event cameras
The project will focus on exploring the use of event-based cameras in neural-based scene reconstruction and synthesis, extending available approaches to event-based data.
Efficient Processing of Event Data for Deep Learning
This project investigates new paradigms for low-level event data processing (from event cameras) to enable expressive and efficient feature extraction.
Wnt signaling activity after Methylphenidate and Omega-3 treatment in human iPSC-derived neural stem cells from ADHD patients
Our lab is looking for a highly motivated Master student (6-12 months), who is interested to conduct a research in the field of disease modeling using ADHD patient-specific cells. The process will involve learning how to generate and culture human induced pluripotent stem cells (iPSCs) and generation of neural stem cells (NSCs) from patients with Attention-Deficit Hyperactivity Disorder (ADHD), as well as how to scientifically interpret and discuss scientific papers and conduct research with independence and critical thinking.

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