Machine Learning Intern

• First-hand experience with biological data, preferably using computational approaches. • Passion for learning how to work with diverse functional genomic assays (RNA/DNase/ATAC/ChIP-seq, etc). • Interest in learning how to analyze single-cell RNA-seq data. • Solid understanding of computational chemistry, including virtual screening (classic QSAR modeling, structure based drug-discovery), library design, etc. • Demonstrated ability to use and develop cutting edge statistical and machine learning methods inspired by real problems. • Experience with machine and deep Learning frameworks (e.g., scikit-learn, PyTorch, etc.). • Demonstrated ability to write high-quality, production-ready code (readable, well-tested, with well-designed APIs). • Experience in Linux environment, database languages (e.g., SQL, No-SQL) and version control practices and tools such as Git. • Publications of high-quality work in relevant computational biology, bioinformatics, systems biology, life sciences, or biomedical venues, including journals and conferences. • Passionate about solving problems, asking questions and learning independently. • Familiarity with the SciPy/PyData ecosystem (numpy, pandas, scipy, dask etc.). • Familiarity with cloud computing services (AWS or GCP). • Familiarity with statistical analysis software, e.g., R.