Supercomputing and Machine-Learning for Big Biomedical Data

We are an interdisciplinary research group in Machine Learning and High-Performance Computing focusing on applications-driven algorithmic research, primarily in the area of computational and systems biology. We are particularly interested in solving big data problems in high-throughput proteomics, genomics and connectomics using variety of high-performance architectures and artificial intelligence, machine-learning and deep-learning strategies.
Our immediate goal is to design machine learning architectures and high-performance algorithm in an efficient scalable fashion to enable discoveries in genomics and proteomics underpinnings of mental disorder. This requires developing an infrastructure to process and analyze high throughput next generation sequencing (NGS) data, big proteomics mass spectrometry data, and fMRI data for connectomics.
We are looking for motivated students willing to pursue PhD, (finishing bachelors this semester or have already finished) with research interests in Machine Learning, Deep Learning, High performance computing, Parallel Programming, and Reconfigurable Architectures. Master’s degree or Background in computational biology/machine-learning/parallel-computing is preferred but not required.