Undergraduate computational cancer research positions for those with quantitative, computational or data-science backgrounds
Undergraduate research in quantitative cancer biology and medicine (1-2 openings)
Dr. Ken Chen, Bioinformatics and Computational Biology, UT MD Anderson Cancer Center
Quantitative science training (math/stats/physics/CS/engineering) desired
Undergraduate researcher in computational cancer biology (1 position)
Drs. Jason Huse and Kasthuri Kannan, Translational Molecular Pathology, UT MD Anderson Cancer Center
Experience in programming in Unix environment, particularly bash scripting preferred
Explore AI and medical big data in the School of Biomedical Informatics (up to 6 positions)
Dr. Degui Zhi, School of Biomedical Informatics, UT Health Science Center
Background in computer/data science is preferred, Python programming skills required
See below for full information about each opportunity:
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Undergraduate research in quantitative cancer biology and medicine
Professor: Ken Chen, Ph.D.
Departments: Bioinformatics and Computational Biology
Institution: The University of Texas MD Anderson cancer center
Openings: 1-2 openings. Starting any time (flexible). Part-time 9-12/hr per week. Offering research mentoring/credit. Financial compensation is available and can be discussed based on job definition. Expect students with quantitative science training, e.g., math/stats/physics/CS/engineering, etc. interested in modeling biological problems/data (e.g., stratify populations, identify hidden factors, reduce technological noise, develop bioinformatics tools, etc.). It can be full-time internship during summer as a paid position (if job duty/projects are defined/agreed).
Lab focus: Developing computational approaches to analyze large-scale cancer omics data and applying them on various cancer investigations. The approaches often involve machine-learning, statistical inference, numerical optimization, model fitting, programming, etc. The applications often include omics profiling, pathway/gene-set analysis, phylogenetics, structural modeling, statistical association, cohort statistical analysis in cancer genomics, targeted therapy and immunotherapy.
Techniques: Computer programming: Python, Perl, java, C, etc. Machine-learning: pyTorch, neural net, etc. Math/statistical analysis: R, matlab, etc. High performance computing: LSF, SunGrid, etc.
Location: Picken’s tower, 1400 Pressler St. 20 min walking distance to Rice. Shuttles available.
Environment/ Personnel: The lab currently is composed of 5 graduate students and 5 postdocs. Has physical seating places on Picken’s, however, has been meeting mostly virtually this year. We have biweekly group meetings and weekly one-on-one meetings. We use Slack for instant communication and information/material sharing. Students can be advised directly by the professor or any members of the lab. Members are from diverse cultural background and academic majors including cancer biology, computer science, math/statistics, biochemistry, etc.
Student research philosophy: Students can apply their learning from classroom on solving real cancer, medical problems. Students should have interest in both quantitative science and medical science, be a quick/versatile learner and able to nurture cross-domain communications and applications. We provide a top-notch training/mentoring opportunity for developing a rewarding career into future medicine and artificial intelligence.
To apply/Contact information: Send an introductory email and attach a CV to Dr. Ken Chen, kchen3@mdanderson.org. Link to our lab web page: https://sites.google.com/site/kchengenomics/
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Undergraduate researcher in computational cancer biology
Professor: Drs. Jason Huse and Kasthuri Kannan
Departments: Translational Molecular Pathology
Institution: University of Texas MD Anderson Cancer Center
Openings: We are seeking one undergraduate researcher to assist in integrative data analysis for a cancer genomics project. This individual would be able to start as soon as possible and the work term is expected to be at least one year. Compensation for work is possible. The anticipated time commitment is approximately 9-12 hours per week and remote working will likely be feasible. Experience in programming in Unix environment, particularly bash scripting, is preferred.
Lab focus: Our group studies the molecular pathogenesis of primary and secondary brain tumors, with an eye toward the development of more effective therapeutics. We are particularly interested in transcriptional and epigenetic regulation, and how these process influence pathogenic events in malignant brain tumors.
Techniques: We use a variety of in vitro and in vivo approaches, coupled with computational analyses. We also collaborate extensively with other scientists within the TMC.
Location: We are located in the Life Science Plaza (2130 W. Holcombe Blvd.) on the 12th floor.
Environment: Our group is currently composed of 3 post-doctoral fellows, two technicians, one graduate student, one medical resident, and one Rice undergraduate. Moreover, our research space melds with that of other, similarly focused groups, creating a rich, diverse, and exciting work environment.
Student research philosophy: In our interactions with undergraduates, our primary focus is their education and ensuring that they fully understand the value of their work and how it fits into the larger investigative narrative of the lab. We understand that specific tasks can often seem disjointed from the “Big Picture” and see our role as providing that necessary context. We enjoy regular interactions with our lab members.
To apply: Please contact Jason T. Huse, MD, PhD, Email: jhuse@mdanderson.org, phone: 713-745-3186. Please include a description of relevant course work and research experience (if any) and a brief statement of your research interests.
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Explore AI and medical big data in the School of Biomedical Informatics, UTHealth, Houston
Professor: Degui Zhi, Ph.D
Departments: School of Biomedical Informatics
Institution: University of Texas Health Science Center at Houston (UTHealth)
Openings: Up to 6 positions are available starting January, 2021 for multiple semesters. Also, 2-4 full-time or part-time paid positions will be available during summer. Students will gain exposure to research projects on big data analysis for population genetics data, imaging genetics, and/or electronic health record data. Background in computer science and data sciences is preferred. Programming skills in Python are required.
Lab focus: The Zhi Group is interested in using big data to advance precision medicine and health. We develop new algorithms and models for the big data collected from biobanks and electronic health records (EHRs), and make new insights that are not possible with smaller scale data. Three main areas of the lab are:
1. Population genetics informatics–We are developing methods to calculate genetic relatedness in large epidemiological cohorts to detect portions of the genome that are linked to diseases. Modern biobanks include genotypes up to 0.1%-1% of an entire large population. At this scale, genetic relatedness among samples is unavoidably ubiquitous. However, current methods are not efficient for uncovering genetic relatedness at such a scale. We developed ultra-efficient methods [https://github.com/ZhiGroup/RaPID] for detecting Identical-by-Descent (IBD) segments, a primary embodiment of genetic relatedness. Our RaPID method detected all IBD segments over a certain length orders of magnitude faster than existing methods, while offering higher power, accuracy, and sharper IBD segment boundaries. We believe identifying IBD segments in population scale cohorts are the first step towards construction population scale genealogy which will be a fundamental infrastructure for future human society.
2. Modeling of electronic health record (EHR) using deep learning–Patients’ health records and other health information are being collected and becoming available. This allows developing representation models that describe the inherent health status and treatment history of a patient. With access to multiple EHR databases with over 50 Million patients, We develop deep learning methods for uncovering the logic of medical practice and to help improve efficiency of clinical care.
3. Imaging genetics—We use deep learning models to extract useful representations from medical images as phenotypes, and conduct genetic studies of such phenotypes.
Techniques: The lab uses techniques typical to bioinformatics and big data research. Senior students will get exposure to deep learning modeling and algorithm development.
Location: 7th floor, SBMI extension of 7000 Fannin St. Walking distance from Rice.
Environment/ Personnel: We are a vibrant lab composed of around 6 graduate students and postdocs. All members are experienced in mentoring graduate students or undergraduates. We have been hosting summer undergrad researchers from Rice University for multiple years.
Student research philosophy: Students will learn computational thinking and skills through problem-solving and critical thinking. My lab will give students exposure of cutting-edge big data research. The students are expected to be able to complete focused independent projects as well as learn team-working.
To apply/Contact information: Interested students should send resume to Degui Zhi, Ph.D., Email degui.zhi@uth.tmc.edu; Website: https://zhigroup.github.io.
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