Postdoctoral Fellow, Computational Genomics

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

Calico is inviting applications from computational biologists for a postdoctoral fellowship position at the intersection of functional genomics and machine learning. The successful candidate will develop machine learning methods to infer and map gene regulatory networks by combining two rich information sources – the DNA sequence surrounding each gene and the observed covariance in single cell genomics experiments. This postdoctoral fellow will be mentored by Principal Investigator David Kelley.

Mapping gene regulatory networks remains a key unsolved problem in computational biology. Effective methods to map gene regulatory networks will enable both inference of network activity and prediction of cellular responses to genetic perturbations, each useful to address diverse biological questions. As a postdoctoral fellow, your primary responsibility will be to conceive and implement approaches to address this gap in knowledge using machine learning methods. You will collaborate closely with both computational and experimental biologists in our laboratories to develop new algorithms and collect relevant experimental data. In addition, you will benefit from collaborations with Calico’s world-class Computing and Genomics teams and our parent company Alphabet’s world-class computing infrastructure.

Postdoctoral fellows at Calico receive mentorship and support to develop independent research programs to enable the next stage of their careers. Postdoctoral fellows will benefit from collaboration with diverse scientists across Calico’s Research and Technology groups, in addition to direct mentorship from their host laboratories. Postdoctoral fellows are encouraged and supported to communicate their findings through high-impact, peer-reviewed scientific journals and scientific conferences.

For examples of our group’s approach, please see our recent publications:

• Avsec et. al. Effective gene expression prediction from sequence by integrating long-range interactions. Nature Methods. 2021. doi: 10.1038/s41592-021-01252-x
• Yuan & Kelley. scBasset: Sequence-based modeling of single cell ATAC-seq using convolutional neural networks. bioRxiv. 2021. doi: 10.1101/2021.09.08.459495 
• Kimmel & Kelley. Semi-supervised adversarial neural networks for single cell classification. Genome Research. 2021. doi: 10.1101/gr.268581.120
• Agarwal & Kelley. The genetic and biochemical determinants of mRNA degradation rates in mammals. bioRxiv. 2022. doi: 10.1101/2022.03.18.484474

Position Requirements

• PhD in computational biology, bioinformatics, computer science, or a related discipline
• 3+ years of experience programming in Python or related data science-oriented languages
• Experience analyzing genomics experiments (e.g. RNA-seq, ATAC-seq, ChIP-seq, scRNA-seq).
• Experience applying machine learning methods to biological problems
• Excellent working knowledge of experimental design and statistics
• Track record of productivity and effective collaboration, clear verbal and written communication skills

Nice to haves

• Experience implementing machine learning models in Tensorflow, PyTorch, or Jax
• Excellent working knowledge of genome biology, gene expression regulation, and epigenetics

Calico requires all new hires to be fully vaccinated for COVID-19 prior to the first date of employment. As required by applicable law, Calico will consider requests for Reasonable Accommodations.