We are not currently looking for masters students. Open positions will be announced as they become avilable

We are looking for individuals who are interested in performing convergent research in Computational Life Sciences particularly for applications of ML/AI modeling.
Computational life sciences refer to the multidisciplinary field that applies computational techniques and tools to solve complex biological and life science problems. This field merges elements from computer science, mathematics, statistics, and engineering with biology, biochemistry, genetics, ecology, and other life science disciplines. The goal is to understand life processes through the analysis and modeling of biological data. Here are some key aspects and applications of computational life sciences:

Population Biology, Genomics and Bioinformatics

• Analyzing DNA sequences, gene expression, and genetic variations to understand the genetic basis of diseases, evolutionary biology, and species diversity, speciation and species adaptation.

Proteomics

• Studying the structure, function, and interactions of proteins which are vital to understanding cellular processes and developing new therapies.

Systems Biology & Multimodal Data Integration

• Integrating data from various omics studies (genomics, proteomics, metabolomics, etc.) to model and understand complex biological systems and networks.
• Integrating data from various High-throughput plant phenotyping (HTPP) data sets with omics data layers to generate comprehensive system-level functional genomics studies.
• Integrating various GIS data layers for understanding GxExM interactions.

Computational Behavioral Neuroscience

• Modeling mechanisms of cognition, neural development, behavior, and genetic mapping of these in model systems.

Ecological Modeling

• Applying computational techniques to understand ecological dynamics and predict changes in ecosystems, and biodiversity patterns, in the context of environmental perturbations, particularly under various climate change scenarios.

Socio-agro-ecological Public Health and Wellbeing

• Analyzing physical and mental social well-being patterns in rural farming communities with respect to agricultural sustainability and agroecosystem management practices and economic landscape.

Geospatial Environmental Characterization for GxExM Studies

• Geospatial characterization of regional environmental characteristics for genetic performance evaluation for variety development and management practices.