The next generation of cancer therapies will aim to prevent cancer. This position is in the lab of Professor Sanju Sinha at the Sanford-Burnham-Prebys Medical Discovery Institute in San Diego. The lab is developing machine learning techniques at the intersection of aging and cancer to discover new preventative therapies.
We need the world’s best minds to solve this problem. If the challenge of developing preventative therapies by understanding the interplay between aging and cancer excites you and you possess strong machine learning skills, we believe you could make an enormous contribution.
This non-traditional role is not a typical academic-focused postdoc, but one that focuses on cultivating skills for transitioning to industry roles, building proprietary technologies, and creating spin-offs based on your research.
The high-level goal of this project is to understand the interplay between aging and cancer by employing state-of-the-art computational biology tools on large-scale multi-omics data from diverse healthy and diseased tissues. The project may include analyzing multiome bulk, single-cell and spatial sequencing, using non-deep and deep machine learning techniques, and employing high-performance computing.
Duties And Responsibilities
- Develop and employ state-of-the-art computational tools to advance our understanding of the interplay between aging and cancer or propel drug discovery.
- Lead and write projects semi-independently.
- Develop and employ machine learning techniques to analyze experimental results, refine the model based on results, and inform future experimental directions.
- Analyze large-scale multi-omics data, both bulk and single-cell, including RNA-seq, exome, genome sequencing, methylation profiles, and other clinical metadata.
- Collaborate and synergize with other members of the lab.
- Report and present research findings and developments clearly to both computational scientists and biologists.
- Initiate and maintain collaborations with other labs worldwide.
Write grants semi-independently for your project and career development.
Education and/or Experience:
- Ph.D in Bioinformatics, Biostatistics, Computer Scicene or Life Sciences plus one year direct experience or Master’s degree and three years experience or an equivalent combination of education, training and/or experience from which comparable knowledge, skills and abilities have been attained required.
- One year experience and/or expertise in computational biology is required.
- Experience with handling, analyzing, and interpreting large omics data is required. This includes bulk and single-cell RNA-seq, DNA-seq, and methylation.
- A proven track record in machine learning using non-deep or deep learning techniques, including designing new architectures, hands-on experimentation, analysis, and visualization.
Other Knowledge, Skills and/or Abilities:
- Effective communication and presentation skills for both technical and non-technical audiences.
- Knowledge of statistical methods applied to biology.
- Strong organizational skills and ability to collaborate well with team members.
- Strong knowledge of linear algebra, calculus, and statistics.
- Experience with version controlling of codes with a code repository like GitHub or equivalent.
- Experience in any of the following is a plus: large-scale deep learning, generative models, graph neural networks.
How To Apply:
Please apply here: https://lnkd.in/gJWsx2gK or email directly to ssinha@sbpdiscovery.org
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Computational Biologist Vacancy, Computational Biologist Vacancy
