All data has shape, and embedded within all shapes is data that can be extracted. Shapes can be obvious and apparent to the eye, like the morphology of plants. Or, shapes can be more abstract, like that of a gene expression network. We are seeking an individual passionate about applying Topological Data Analysis (TDA), a mathematical method that measures shape, to the plant sciences. The successful candidate will be a pioneer, applying state-of-the-art data science techniques and mathematical approaches to the analysis of intensive datasets and predictive modeling.
The project is based at Michigan State University between the laboratories of Professors Beronda Montgomery, Arjun Krishnan, Elizabeth Munch, and Dan Chitwood together with collaborator Aman Husbands (Ohio State University). The postdoc will work with all five PIs, who offer complementary scientific and mathematical expertise. Between the PIs, the candidate will have access to expertise including Topological Data Analysis, network biology, machine learning approaches, bioinformatics, molecular biology, and plant growth and development. The PIs are committed to the career development of the candidate who will be mentored in line with their professional goals.
We live in extraordinary times during the COVID-19 pandemic. The circumstances of employment, including start date, possibility of remote work, and access to on-campus resources, will be discussed with the candidate to accommodate their health, well-being, and success on the project.
The post-doc will lead data analysis efforts in: 1) creating and processing plant growth time lapse images from 3D X-ray Computed Tomography (CT) data, 2) processing corresponding gene expression time series data using RNA-Seq and constructing gene networks, and 3) applying Topological Data Analysis (TDA) to plant morphology and gene networks, with the long-term goal of predictively modeling each from the other using machine learning approaches. This project will focus on the model plant Arabidopsis, utilizing accessions with contrasting developmental stability and leaf morphology, as well as differing light regimens that elicit plastic changes in plant growth and gene expression.
A PhD with a strong background in mathematics, network biology, computational biology, computer science, machine learning, or other closely related fields. Good communication skills, willingness to collaborate openly, give/take constructive feedback, and sustain a friendly and collegial workplace environment.
Programming experience in Python, R, and/or C++ with version control (Git), and experience with high-performance cluster computing.
Required Application Materials
A CV, 2) A statement of your research interests (≤ 500 words), and 3) Names & contact information for three references.
Review of applications will begin August 6, 2020, and will continue until the position has been filled.
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