Computational Research Assistant / Scientific Data Curator
School of Medicine, Stanford, California, United States

Schedule: Full-time
Job Code: 4181
Employee Status: Regular
Grade: F
Requisition ID: 92844

The Kundaje lab develops statistical and machine learning methods for large-scale integrative analysis of functional genomic data to decode regulatory elements and pathways across diverse cell types and tissues and understand their role in cellular function and disease.

We have led the analysis efforts of the Encyclopedia of DNA Elements (ENCODE) and The Roadmap Epigenomics Projects with the development of novel methods for

1. Denoising and normalization of large-scale functional genomic data.
2. Dissecting combinatorial transcription factor co-occupancy within and across cell-types.
3. Predicting cell-type specific enhancers from chromatin state profiles.
4. Modeling 3D genome architecture and predicting cell-type specific enhancer-promoter interactions.
5. Learning transcriptional regulatory networks that integrate proximal and distal cis and trans signals.
6. Improving the detection and interpretation of potentially causal disease-associated variants from genome-wide association studies.

More recently, we have also been developing

1. Interpretable deep learning frameworks for functional genomics and epigenomics.
2. Causal regulatory models by integrating functional genomic data from temporal (e.g. differentiation/reprogramming) and perturbation (e.g. drug response, knockdown, genome-editing) experiments.
3. Early cancer detection and tissue-of-origin deconvolution from liquid biopsy (e.g. cell-free DNA) assays.
4. Methods to understand the relationships between genetic variation, regulatory chromatin variation and expression variation in healthy and diseased individuals.

We are a highly interdisciplinary group, and lab members originate from diverse backgrounds including computer science, pure and applied mathematics, genetics, computational biology, biomedical informatics, and even biophysics. We collaborate extensively with other labs at Stanford and beyond to decipher genome function by integrating cutting-edge computational and experimental approaches.

We are seeking a Computational Research Assistant to join our core team.

Duties include:

• Develop and apply state-of-the-art statistical and computational methods to interpret high-throughput epigenomic measurements from relevant model systems, and will be responsible for maintaining and improving established pipelines for omics analysis and interpretation.
• Contribute to the evaluation of performance of high-throughput pipelines and manage/process large genomic datasets.
• 3D chromosomal architecture literature is growing rapidly and it's a challenge to keep with all the advances in the field. Keep up to date with current methods, and apply relevant methods to existing research.
• Assist in building robust, scalable tools for data cleaning and transformation of raw datasets into inputs for machine learning algorithms.
• Collaborate with domain scientists to identify computational bottlenecks and implement solutions, and evaluate performance.
• Engagement with broader scientific community, societies, committees, and councils
• Put together documents to summarize results and/or to present at conferences.

*Other duties may also be assigned.

DESIRED QUALIFICATIONS:

• Knowledge of classical supervised and unsupervised machine learning algorithms (gradient-boosted trees, Random Forest, k-means clustering, support vector machine-based methods, linear and logistic regression, etc.).
• Comfortable working with Unix-based systems and SLURM-based compute cluster environments.
• Familiarity with standard programming practices (version control with git, modular software design, unit test frameworks such as Snakemake, ReadTheDocs documentation).
• Familiarity with Singularity and Docker container deployment and maintenance.
• High competence with the Python programming language.
• Familiarity with R or similar statistical analysis/ visualization framework.
• Comfortable working with SQL and NoSQL databases (MySQL, tileDB, MongoDB, other triplestore and RDF databases).
• Strong software engineering skills.
• Strong communication skills.
• Comfort with a high learning curve and rapidly changing code-base.
• Team player.

EDUCATION & EXPERIENCE (REQUIRED):

Bachelor's degree in scientific field and one year of related experience, or combination of relevant experience.

KNOWLEDGE, SKILLS AND ABILITIES (REQUIRED):

• Solid analytical skills.
• Strong written and oral English communication skills.
• Computer skills, including word processing and spreadsheet applications.
• Ability to understand scientific literature, experimental procedures and their limitations, and current needs of the research community.
• Knowledge of classical supervised and unsupervised machine learning algorithms (gradient-boosted trees, Random Forest, k-means clustering, support vector machine-based methods, linear and logistic regression, etc.).
• Comfortable working with Unix-based systems and SLURM-based compute cluster environments.
• Familiarity with standard programming practices (version control with git, modular software design, unit test frameworks such as Snakemake, ReadTheDocs documentation).
• Familiarity with Singularity and Docker container deployment and maintenance.
• High competence with the Python programming language.
• Familiarity with R or similar statistical analysis/ visualization framework.
• Comfortable working with SQL and NoSQL databases (MySQL, tileDB, MongoDB, other triplestore and RDF databases).
• Strong software engineering skills.
• Strong communication skills.
• Comfort with a high learning curve and rapidly changing code-base.
• Team player.

CERTIFICATIONS & LICENSES:

None.

PHYSICAL REQUIREMENTS*:

• Frequently perform desk based computer tasks, seated work and use light/ fine grasping.
• Occasionally stand, walk, and write by hand, lift, carry, push pull objects that weigh up to 10 pounds.

* - Consistent with its obligations under the law, the University will provide reasonable accommodation to any employee with a disability who requires accommodation to perform the essential functions of his or her job.

WORKING CONDITIONS:

• May work extended or non-standard hours based on project or business needs. Occasional local travel may be required.
• Some work may be performed in a laboratory setting.

WORK STANDARDS (from JDL):

• Interpersonal Skills: Demonstrates the ability to work well with Stanford colleagues and clients and with external organizations.
• Promote Culture of Safety: Demonstrates commitment to personal responsibility and value for safety; communicates safety concerns; uses and promotes safe behaviors based on training and lessons learned.
• Subject to and expected to comply with all applicable University policies and procedures, including but not limited to the personnel policies and other policies found in the University's Administrative Guide, http://adminguide.stanford.edu.

As an organization that receives federal funding, Stanford University has a COVID-19 vaccination requirement that will apply to all university employees, including those working remotely in the United States and applicable subcontractors. To learn more about COVID policies and guidelines for Stanford University Staff, please visit https://cardinalatwork.stanford.edu/working-stanford/covid-19/interim-policies/covid-19-surveillance-testing-policy.


To be considered for this position please visit our web site and apply on line at the following link: https://apptrkr.com/2781988

Stanford is an equal opportunity employer and all qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by law.







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