GA adertisement

Graduate researcher positions are available to apply data science and machine learning to predict new coronavirus variants, develop interpretable deep learning methods to predict biological clocks and diseases. Candidates are expected to code in R and/or Python and have good writing skills. Our group’s recent publications appeared in Scientific Reports, GeroScience, and BMC Bioinformatics. Students from all backgrounds are welcome, as long as they have strong interests in the multi-disciplinary research projects and have sufficient skills. To apply, please send your resume, transcripts, and relevant supporting materials such as past coding projects or essays to hong-qin@utc.edu 

PhD positions

 My lab has multiple PhD positions open. Research directions are in Data Science, machine learning, and biomedical big data. One research direction is to develop multi-view deep learning neural networks to integrate heterogeneous genomics data sets to predict aging and diseases. The second research direction is to develop MASK-RCNN models to detect and quantify cell objects, and develop graph-based algorithms to infer cell division events. The third research direction is to apply algebraic graph theory and develop deep-learning methods for single-cell genomics data analysis. Lab GitHub projects can be seen at github.com/hongqin

Please contact hong-qin@utc.edu or qinstat@gmail.com with your resume, transcripts, personal statement, and references.

biology student researcher advertisment

 Biology/Biochemistry major needed for a gene network + deep learning research project on longevity modeling and prediction

I am looking for an undergraduate biology/biochemistry major interested in teaming up with computer science students to work on using deep learning to model gene networks and lifespan of cells. A relevant publication is “Using deep learning to model the hierarchical structure and function of a cell”, Nature Methods, 2018, https://www.nature.com/articles/nmeth.4627.

Relevant knowledge of the biology/biochem student may include a good understanding of the molecular mechanisms of genetic materials, replication, gene expression, protein biosynthesis and function, metabolism, cellular organization. Most importantly, this student researcher should be eager to learn new concepts and skills. The biology student is expected to work on the molecular biology aspect of this inter-disciplinary project, and receive hourly pay.  Computational skill is not required for this biology/biochem student, but we hope he/she is willing to learn computational skills. 

If interested, please send your resume and unofficial transcripts to xxx@utc.edu

FreightTech Innovation Challenge: A 24-Hour Transportation and Logistics Use Case Competition,

I’m excited to announce the inaugural FreightTech Innovation Challenge: A 24-Hour Transportation and Logistics Use Case Competition, taking place March 29–30, 2019 in Chattanooga, TN. Presented in collaboration with FreightWaves and CO.LAB, this event is a 24-hour competition for college students from across the nation to team up and take on some of the greatest challenges in transportation and logistics.

From now until March 8, we invite students who are interested in business, supply chain, technology, computer science, data, and logistics (and more) to apply for this immersive experience that could ultimately impact their career paths. Participating students will work directly with major industry players and experts to find solutions to real challenges in the industry, with a chance to win cash prizes and find potential future employers.

Here are the benefits for participating students:

·  Opportunity to work closely with a team of student peers and industry experts to solve unique industry challenges

·  Network with fast-growing companies and find potential future employers

·  Chance to win a cash prize and gain recognition for yourself and your university (cash prizes awarded to top three teams: 1st Place - $5,000; 2nd Place - $3,000; 3rd Place - $1,000)

·  Weekend to discover Chattanooga, TN—a leading hub for the transportation industry—also known as “Freight Alley” (participating teams will receive housing vouchers)

We ask that all students interested apply at colab.co/freighttechchallenge, where they can indicate whether they are part of a team or applying as an individual. 

You can find more information at colab.co/freighttechchallenge or on the information sheet attached to this email. Included on the information sheet is the schedule (may be subject to change) that outlines the activities for the weekend. Please let me know if you have any questions.

Why Chattanooga?

Chattanooga—known as the Scenic City due to its beauty and outdoor recreational scene—has grown to become athriving new hub for startups and large companies alike. With a supportive ecosystem for businesses, Chattanooga has also established itself as the heart of “Freight Alley,” with an increasingly growing number of companies in the transportation and logistics industry due to its position as the epicenter of freight traffic in the Southeast.

“If you start a company there to serve the trucking industry, you have more expertise about what the needs are, and more customers and partners there in Chattanooga as opposed to New York City, Boston and San Francisco. Chattanooga is the “Silicon Valley” of Trucking”
— Steve Case, AOL Co-Founder and Co-Founder of Rise of the Rest

Sponsored by U.S. Xpress, Reliance Partners, Arrive Logistics

Qin lab brief description

Dr. Hong Qin's group uses computational and mathematical approaches to investigate biomedical and biological questions.  One focus is to develop probabilistic gene network models to infer network changes during cellular aging. We build gene network models from heterogenous genomics data sets, including protein interactions, gene expression data sets, RNAseq data sets, protein mass-spec data sets, high-throuput phenotypic screens, and gene annotations.  We are developing machine-learning methods to automatically estimate cellular lifespan from time-lapsed images. We are also applying engineering principles to study molecular, biological, and ecological networks. We are developing deep-learning methods for better classification and prediction using heterogenous biomedical and biological large data sets. Dr. Hong Qin is a recipient of a NSF CAREER award 2015-2020.

Qin's expertise: Graph reliability modeling; Bioinformatics; Computational genomics; Mathematical modeling; Systems Biology;  Cellular aging; Gene network analysis and modeling.