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Student Research Assistant Positions

For MIDAS Affiliated Faculty:
If you are a faculty member and would like to submit a research project you are looking for student assistance/collaboration on, please submit the below form:

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The following projects are seeking student research assistants (click name to expand):

Antibiotic Resistance & Drug Combination Discovery (Sriram Chandrasekaran, Biomedical Engineering)

Antibiotic resistance & drug combination discovery
Project Summary
The focus of this project is to understand antibiotic resistance and design novel drug treatments. 100,000 people die and a million others are sickened by antibiotic resistant bacteria in the United States every year. There is an urgent need to develop high-throughput approaches to screen promising drugs to counter antibiotic-resistance. The student will apply computer algorithms developed in our lab to identify potent antibiotic combinations for treating drug resistant microbial infections.
Required skills
Familiarity with MATLAB or Python programming. Basic knowledge of microbiology and genetics. Knowledge of machine learning is a plus.

Compensation
Students may register for independent research credit.

Responsibilities
The project involves data collection, model construction, simulation, analysis, testing,  and literature review. Estimated 8 hours per week of work.
Faculty mentor
Sriram Chandrasekaran, Ph.D.
Date Posted: 10/14/2021

Fall GSRA Position Announcement in Computational Social Science Course Development (Bruch, Jacobs, and Romero)

Fall GSRA Position Announcement 

COMPUTATIONAL SOCIAL SCIENCE COURSE DEVELOPMENT

Professors Bruch, Jacobs, and Romero are developing a Computational Social Science class for undergraduate students with little or no programming experience. We are looking for a graduate student to assist us in developing Jupyter notebooks, lectures, and labs that introduce students to basic Python programming and CSS methods for text analysis, network analysis, and machine learning. 

The position will be funded through a GSRA position at 20 hours/week, which will cover tuition and stipend at standard rates. It will run through Fall semester and possibly Winter semester. You will be working closely with the faculty leads, as well as four undergraduate RAs who will be testing the modules, providing input on content, and otherwise assisting with course development. 

Qualifications: We are looking for a graduate student from Computer Science, Information, Data Science, Quantitative Social Science, or some related area with relevant experience with data analysis or computational social science. Experience in Python is required; the ideal candidate would have exposure to tools for doing social network analysis, machine learning, and/or text analysis and a strong interest in computational social science. Enthusiasm for developing innovative, hands-on educational materials is a must. Experience teaching and/or delivering hands-on teaching materials for undergraduate courses is desirable but not required. 

How to apply: Please email your cover letter describing your interest and fit for the position, CV, and a copy of your unofficial transcript to Daniel Romero (drom@umich.edu ) and Elizabeth Bruch (ebruch@umich.edu ). 

Deadline to apply: We will be reviewing applications as they come until the positions are filled.

Date Posted
5/07/2021

The Laboratory of Integrated Brain Imaging (PI: Dr. Zhongming Liu)

The Laboratory of Integrated Brain Imaging (PI: Dr. Zhongming Liu) has multiple positions available for undergraduate and graduate students to work for research during Summer 2021. As a temporary worker (or a summer intern), each student will be paid for on an hourly basis. Preference will be given to the students who are likely to extend the work to Fall 2021 or research for master thesis.

Job 1: Animal MRI image analysis

The student will work with the PI and existing graduate students to develop and test an MRI image processing pipeline for automated analysis of gastrointestinal MRI. The pipeline to be established will include image denoising, co-registration, motion correction, segmentation, 3D surface modeling, rendering. A strong candidate should have strong programming skills (Python and Matlab), knowledge with image and signal processing and optimization, good work ethics and commitment to teamwork. An ideal candidate should have experiences with machine learning, especially deep learning (using PyTorch).

Job 2: Animal fMRI analysis pipeline

The student will work with the PI and existing graduate students to develop and test a processing pipeline for online and offline analysis of animal fMRI images. The pipeline to be established will include image denoising, filtering, motion correction, co-registration, segmentation, surface modeling and rendering, as well as statistical parametric mapping with linear regression, statistical tests, principal or independent component analysis, functional connectivity etc. A strong candidate should have strong programming skills (Python and Matlab), knowledge with image and signal processing, optimization methods, good work ethics and commitment to teamwork. An ideal candidate should have knowledge with either animal or human brain mapping, system neuroscience.

Job 3: Representation learning of fMRI activity

The student will work with the PI and existing graduate students to develop, test and document deep learning methods for representation learning of human brain activity. The methods to be developed will include deep learning with neural networks, e.g., variational autoencoder, recurrent neural networks, Transformer, graph neural networks. Part of this job will involve documentation, testing, and application of existing methods already developed in the lab. A strong candidate should have strong programming skills, prior experiences with deep learning (PyTorch or TensorFlow) in the context of medical image analysis, computer vision or natural language processing, good work ethics and commitment to teamwork.

Job 4: Animal MRI and electrophysiology

The student will need to work in the lab and assist in vivo animal experiments that involve animal (rodents) MRI, neuromodulation, and electrophysiology. A strong candidate should have interest and background in biomedical engineering and neuroscience. An ideal candidate should have prior experiences with animal handling, surgery, behavioral testing, electrophysiology. Experiences with data analysis and programming are desirable but not required.

For any inquiry, please contact Dr. Zhongming Liu (zmliu@umich.edu). 

Date Posted
4/15/2021

Cancer Metabolism and Precision Medicine (Sriram Chandrasekaran, Biomedical Engineering)

Project Summary
This project involves the application of computer models to simulate the metabolic properties of tumors. The computer models will be built using genomics, metabolomics and transcriptomics data from various types of cancer cell lines. By understanding the unique metabolic properties of each cell type, we can design drugs that target specific tumors. Further, knowledge of these differences will be used to design synergistic drug combinations tailored to each patient.

Responsibilities
The project involves data collection, model construction, simulation, analysis, testing,  and literature review. Estimated 8 hours per week of work.

Required Experience
Preferred skills: Familiarity with MATLAB or Python. Basic knowledge of biochemistry, molecular biology and genetics. Experience working with big-data (genomics, transcriptomics) and knowledge of machine-learning.

Compensation
Students may register for independent research credit.

Contact
csriram@umich.edu

Date Posted
10/14/2021

Representation Learning of Brain Activity (Zhongming Liu, Biomedical Engineering, Electrical & Computer Engineering)

Project Summary
Representation learning of brain activity. Learning algorithms are designed to represent and decode brain activity, e.g. to reconstruct human vision, speech, language, or dream. Abundant data are available from human or animal brains.

Responsibilities
The project involves data analysis, writing code, and literature analysis. Estimated 5-10 hours per week of work.

Required Experience
Graduate or senior undergraduate students in computer science, electrical engineering, biomedical engineering, statistics, or mathematics. Ideal candidates should have completed courses related to machine learning, especially deep learning, and experiences with PyTorch or TensorFlow.

Compensation
Hourly pay is available, Students may register for independent research credit

Contact
zmliu@umich.edu

Date Posted
1/19/2021