The Center for Computational Biomedicine is a new center within the Harvard Medical School. Our mission is to provide cutting edge computational capabilities, data analysis and to develop and support large data resources in order to accelerate the adoption and integration of new technologies throughout the HMS ecosystem. Areas of application include imaging, genetics, genomics, clinical data, claims and other data sources.
About the Position
Research Scientist is a new academic, non-faculty track recently established at HMS. The candidate will be either Research Scientist or Senior Research Scientist, commensurate with experience and qualifications.
We are looking for a highly motivated collaborative individual who will help define strategic goals and will provide leadership in the adoption of tools and methods to analyze high-throughput omics data. The ideal candidate will have substantial experience in developing analytic methods for transcriptomics, epigenetics, metabolomics or other high throughput technologies such as CRISPR screens. They will have experience with machine learning approaches that can applied to integrate and learn from these data resources.
A strong background in developing and deploying software solutions for the methods they develop will be essential.
The successful candidate will help build a team that can support complex analyses of large data sets. They will help develop and deploy appropriate AI/ML approaches to analyze and interpret and integrate data sources to provide new insights.
- Providing scientific leadership and strategic planning.
- Managing people and projects.
- Developing algorithms
- Implementing algorithms.
- A PhD in Statistics, Computer Science or Computational Biology with expertise in machine learning or 10 years experience in the field.
- Strong communication skills.
- An ability to manage projects and teams in a dynamic academic environment.
- Expertise in designing and using algorithms to analyze single cell data such as RNA-seq, CITE-seq, spatial transcriptomics, metabolomics.
- Expertise in machine learning methods such as neural networks, Random Forests, Generative Adversarial Networks.
- Experience with TensorFlow or similar platforms.
- Experience with cloud deployment of software solutions.
- A track record of algorithm development accompanied to by robust software development (e.g., Bioconductor packages or Python modules).
- Proven ability to integrate large complex data sources to extract knowledge.
Send letter of interest and CV to Susanne_Churchill@hms.harvard.edu