Massachusetts Institute of Technology

Open Opportunities

Master thesis or technician position: High-throughput investigation of dosage compensation to heterozygous mutations in mammalian systems. Massachusetts Institute of Technology Course 7: Biology There are 300 known diseases that arise from haploinsufficiency (i.e. heterozygous mutations). A recent study reported that the number can be even higher (up to 3000 genes) that display signs of haploinsuffeciency (Collins et al, Cell, 2023). Why heterozygous mutations in some genes cause disease while others do not? Dosage compensation implies increased expression levels of the unaffected allele. In this project, we aim to investigate the extent to which dosage compensation occurs in respond to heterozygous mutations in mammalian systems using high throughput CRISPR screens coupled to single cell RNA sequencing. The project will also aim to investigate the role of the recently discovered Transcriptional Adaptation phenomenon (El-Brolosy et al, Nature, 2019) on dosage compensation, in addition to exploring therapeutic strategies for haploinsufficiency disorders that aim to enhance dosage compensation Animal Physiology-Cell, Animal Physiology-Systems, Cell Physiology, Databases and Ontologies, Gene Expression, Gene Therapy, Genetic Development (incl. Sex Determination), Genetic Technologies: Transformation, Site-directed Mutagenesis, etc., Medical Biochemistry: Nucleic Acids, Medical Biotechnology, Medical Genetics, Other, Population and Ecological Genetics, Quantitative Genetics, Sequencing and Genomics, Systems Biology and Networks, Therapies and Therapeutic Technology Internship, Master Thesis, Semester Project

Neural Implicit Fields for Representing Cardiovascular Organs Massachusetts Institute of Technology Course HST: Health Sciences and Technology This project revolves around creating implicit neural representations of cardiovascular organs for application to virtual patient generation. Artificial Intelligence and Signal and Image Processing, Biomechanical Engineering Master Thesis

Topological Regularization for Generative Models of Cardiovascular Anatomy Massachusetts Institute of Technology Course HST: Health Sciences and Technology This project focuses on implementing topological regularization techniques for generative models of cardiovascular anatomy. Artificial Intelligence and Signal and Image Processing Master Thesis

Interactive Multi-Tissue Segmentation Platform for Intravascular Imaging Massachusetts Institute of Technology Course HST: Health Sciences and Technology This masters thesis project revolves around developing a rapid annotation platform for the creation of patient-specific digital twins from intravascular imaging. Artificial Intelligence and Signal and Image Processing Master Thesis

wireless power transfer systems for ingestible and implantable robots Massachusetts Institute of Technology Course 2: Mechanical Engineering Ingestable and implantable robots that can reside in the human body for long term are revolutionizing the future of personalized medicine. However, one of the most significant challenges facing the widespread adoption of these devices is ensuring a reliable and sustainable power source.Traditional power sources, such as batteries, are impractical for long-term use within these robots due to size constraints, limited energy capacity, and the need for repeated invasive procedures for replacement. In the Traverso Lab at Brigham and Women’s Hospital (Harvard Medical School), We are exploring advanced engineering approaches to develop novel wireless power transfer (WPT) systems as sustainable powering sources. Biomedical Engineering Internship, Master Thesis

Closed-loop control of drug delivery Massachusetts Institute of Technology Course 2: Mechanical Engineering The Traverso lab at Brigham and Women's Hospital, Harvard Medical School currently have several opening positions for students who are interested in interdisciplinary research in translational medicines. We are pushing the frontier of translational medicine by bridging the gap between engineering and clinical medicine. Antenna Technology, Arithmetic and Logic Structures, Automotive Engineering, Composite Materials, Computer Communications Networks, Control Engineering, Databases and Ontologies, Diagnostic Applications, Electrical and Electronic Engineering, Flexible Manufacturing Systems, Mechanical Engineering, Modeling and Simulation, Polymerisation Mechanisms, Polymers, Sensor (Chemical and Bio-) Technology Bachelor Thesis, Lab Practice, Master Thesis, Semester Project

A gastrointestinal mucosa interface for prolonged theranostics Massachusetts Institute of Technology Course 2: Mechanical Engineering The surface of the gastrointestinal (GI) tract is covered by a mucosal membrane, consisting of enormous health-related biochemical, physiologic, and pathophysiologic information, and serving for nutrition exchange. Progress has been made to access the GI mucosa for diagnostics and therapeutics in clinical settings. However, it is still extremely challenging to build a biocompatible and robust GI mucosa interface enabling real-time, continuous, and minimally invasive interactions with human body, due to the constant GI motility, fast cellular turnover rate, limited cavity space and extremely chemical and biological environments In the Traverso Laboratories at Brigham and Women’s Hospital (Harvard Medical School), We are exploring novel engineering approaches to develop robust mucosal interfaces for long-term deployment of micro-electronics/drug reservoirs/physical barriers in the GI tract. Biomaterials, Biomechanical Engineering, Mechanical Engineering, Polymers Master Thesis

Deep Learning-Based Co-Registration of Coronary Computed Tomography and Intravascular Images Massachusetts Institute of Technology Course HST: Health Sciences and Technology Coronary atherosclerosis can be assessed with a wide array of imaging tools, each with its own strengths in diagnosis and treatment planning. However, aligning such imaging modalities is difficult and time-consuming. This project aims to develop a deep learning tool that can achieve multi-modal co-registration of computed tomography and intravascular images of coronary artery disease. Such a tool can be used to rapidly and automatically align large multi-modal imaging datasets, paving the way for various clinical and machine learning applications. Cardiology, Computer Vision Master Thesis