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The Traverso Lab at Brigham & Women's Hospital (Harvard Medical School) is focused on the development of commercializable products and technologies. One of our current interests is in leveraging computational and experimental methods to develop novel food products. We are seeking motivated and independent students to join our lab for their master's thesis. Students will be expected to develop competent physical models, simulate fluidics and mechanics, perform rheological tests, and validate theoretical models with experiments. Experience in these areas is beneficial but not necessary. We aim to recruit students with creativity, a willingness to learn, and the ability to work collaboratively on a team of interdisciplinary scientists and engineers.
- CAD/CAM Systems, Composite Materials, Fluidization and Fluid Mechanics, Food Engineering, Food Processing, Mechanical Engineering
- Internship, Master Thesis
Delayed bone healing or failed non-unions account for 5 – 10% of all bone fractures and present a challenging problem in regenerative medicine. The impact of delayed unions or non-unions can be devastating with prolonged rehabilitation, decreased quality of life and significant health care costs. Our lab has conducted fracture healing studies in young and prematurely-aged mouse models with different defect sizes. The aim of this project is to analyse data from mice which exhibit delayed unions and non-unions.
- Biomaterials, Biomechanical Engineering
- Bachelor Thesis, Internship, Master Thesis, Semester Project
The remarkable agility of animals, characterized by their rapid, fluid movements and precise interaction with their environment, serves as an inspiration for advancements in legged robotics. Recent progress in the field has underscored the potential of learning-based methods for robot control. These methods streamline the development process by optimizing control mechanisms directly from sensory inputs to actuator outputs, often employing deep reinforcement learning (RL) algorithms. By training in simulated environments, these algorithms can develop locomotion skills that are subsequently transferred to physical robots. Although this approach has led to significant achievements in achieving robust locomotion, mimicking the wide range of agile capabilities observed in animals remains a significant challenge. Traditionally, manually crafted controllers have succeeded in replicating complex behaviors, but their development is labor-intensive and demands a high level of expertise in each specific skill. Reinforcement learning offers a promising alternative by potentially reducing the manual labor involved in controller development. However, crafting learning objectives that lead to the desired behaviors in robots also requires considerable expertise, specific to each skill.
- Information, Computing and Communication Sciences
- Master Thesis
Take part in an cutting-edge project aimed at building a novel ultrasound transducer concept. Develop a prototype, model its ultrasound response and test it in our lab.
- Mechanical Engineering, Medical Physics, Robotics and Mechatronics
- Master Thesis, Semester Project
The project's goal is to create cutting-edge algorithms that use low-power ultrasonic sensors with TinyML for precise presence detection and indoor people counting. Additionally, the study intends to investigate sensor fusion with CO2, VOC, noise, or other data to improve accuracy. The study assesses the practicality of the technology, taking into account the trade-offs between precision, power consumption, and system limitations.
- Communications Technologies, Computer Software, Electrical Engineering, Environmental Technologies, Neural Networks, Genetic Alogrithms and Fuzzy Logic, Pattern Recognition, Signal Processing, Simulation and Modelling
- Bachelor Thesis, Energy Harvesting (PBL), Firmware (PBL), Machine Learning (PBL), Master Thesis, Microcontroller (PBL), Semester Project, Software (PBL)
Switzerland is committed to transitioning to a renewable energy system. The Swiss government has set a target of achieving net-zero carbon emissions by 2050. This will require a significant increase in the use of renewable energy sources. The Swiss power grid is also vulnerable to imbalances be-tween supply and demand. Demand flexibility can help to mitigate this risk and ensure the reliable operation of the power grid. Demand flexibility is the ability to shift or reduce energy use in response to changes in sup-ply or price. This is becoming increasingly important as the power grid transitions to renewable energy sources, such as solar and wind power, which are intermittent and less predictable. Demand flexibility can help to balance the grid and reduce the need for expensive and polluting backup power plants. Non-Intrusive Load Monitoring (NILM) and customer segmentation modeling are powerful tools that can be used to develop demand flexibility programs. NILM can be used to identify high-energy-consuming appliances and to track their energy usage over time. Customer segmentation modeling can be used to identify different groups of customers based on their energy consumption patterns. This information can then be used to develop targeted demand flexibility programs that are more likely to be effective for each group of customers.
- Building not elsewhere classified, Building Science and Techniques, Neural Networks, Genetic Alogrithms and Fuzzy Logic, Signal Processing, Simulation and Modelling
- Master Thesis
The primary objective of this project is to develop an automated pipeline for the identification and recognition of patterns within urodynamic recordings, utilizing urodynamic recording data in conjunction with annotated patterns provided by experts. This endeavor seeks to reduce the susceptibility of interpreting urodynamic recordings to potential errors arising from human judgment and inaccuracies, thereby improving the management of urinary tract complications in patients with spinal cord injury. By implementing a systematic approach to pattern recognition in Bladder Valomue/Pressure Time Series Measurements of urodynamic data, the potential for error in decision-making can be significantly reduced.
- Artificial Intelligence and Signal and Image Processing, Biomedical Engineering, Biosensor Technologies, Computer Hardware, Computer-Human Interaction, Electrical and Electronic Engineering, Engineering/Technology Instrumentation, Mechanical Engineering, Medical Biotechnology
- Internship, Master Thesis, Semester Project
Gait patterns in multiple impairments present unique and complex patterns, which hinders the proper quantitative assessment of the walking ability for chronic ambulatory conditions when translated to daily living. In this project, we will focus on finding clusters of gait patterns through unsupervised learning from a large dataset of incomplete spinal cord injury individuals. The goal is to investigate hidden patterns in relation to the type of injuries and find their application for future diagnosis and rehabilitation treatment.
Your work will guide future rehabilitation methods in general clinical practice, through applied classification and dimensionality reduction in Biomechanics of walking.
Goal: Develop an unsupervised clustering pipeline for a large dataset of gait patterns from spinal cord injured individuals for class similarity evaluation
- Engineering and Technology, Expert Systems, Medical and Health Sciences, Neural Networks, Genetic Alogrithms and Fuzzy Logic, Pattern Recognition, Signal Processing, Simulation and Modelling
- Bachelor Thesis, ETH Zurich (ETHZ), Internship, Master Thesis
In this master's thesis project, we are looking for a candidate to apply machine learning techniques to correct and predict signals of incomplete CT perfusion imaging for ischemic stroke. We hope to use machine learning techniques to de-noise and correct for the truncation in CT perfusion signals. In particular, we aim to infer the true attenuation curve after the truncation time-point.
- Artificial Intelligence and Signal and Image Processing, Central Nervous System, Radiology and Organ Imaging
- Master Thesis
This is a clinical image registration and visualization project that tries to map a zoomed-in CT view with a zoomed-out MR modality. The CT view can see very detailed blood vessels and bones, while the MR view sees the soft brain tissues but without vessels. The clinicians want to register them together automatically, as they are currently aligning the two views by hand manually and takes them a lot of time. The outcome of this project is an automated, fast, and accurate image co-registration software that can be deployed in the hospital to improve clinical care.
- Information, Computing and Communication Sciences, Medical and Health Sciences
- Internship, Master Thesis, Semester Project