Calico is seeking an intern to join the Biochemistry and Computing groups. To succeed, you will need to be an enthusiastic team player, detail-oriented, extremely organized and comfortable working on complex problems. Working with the team for macromolecule structural biology studies, you will perform comprehensive protein structure analysis and structure-based biochemistry studies. At the same time, you will partner with our computational team on machine learning-based drug development. As such, the successful candidate will work closely with members of both the biochemistry technology lab and the machine learning team.
Responsibilities:
- Work with protein-structure analysis software packages to design structure-based biochemical/biophysical assays
- Perform assays for protein-protein interaction, protein-small molecule interaction, and enzymatic activity
- Build machine learning models to predict assay results and to optimize experimental parameters
Position requirements:
- Currently pursuing a PhD in computational science, biochemistry, structural biology or related field
- Experience working with machine learning models for sequence data analysis such as those used for analyzing protein sequences and/or natural language processing
- Experience with deep learning frameworks such as TensorFlow, Pytorch or Jax
- Knowledge of protein science principles, including biochemical/biophysical properties of protein molecules
- Experience with protein biochemistry assays
- Experience with protein structure determination approaches such as cryoEM, SAXS, X-ray, or crystallography
- Detail-oriented and organized
- Strong teamwork and communication skills
- Self-motivated with a “can-do” attitude
- Flexible and able to respond quickly to shifting priorities
- Available to work in South San Francisco throughout June, July and August of 2021 (due to the COVID-19 pandemic and its effect on Calico’s business needs and operations, you may be required to work from home and/or your anticipated start date may change or be delayed)
Nice to have:
- Experience with molecular dynamics (MD) simulation methods
- Experience of using active learning approaches to iteratively improve experimental performance