Research Associate - Bioinformatics Trainer and Analyst
Research Associate - Bioinformatics Trainer and Analyst
School Harvard T.H. Chan School of Public Health
The Harvard Chan Bioinformatics Core is excited to expand our bioinformatics training program as part of a new collaboration with the Dana-Farber / Harvard Cancer Center (DF/HCC). We are looking for a bioinformatician to join our team in our efforts to provide education and analytical support to the Harvard community. The ideal candidate is enthusiastic about teaching (as demonstrated by their teaching experience), enjoys working in a collaborative environment, and has a background in high-throughput data analysis, specifically for next-generation sequencing (NGS) data. This role provides a unique and rewarding opportunity to train and support world-class researchers making a profound impact on human health.
Located at the Harvard T.H. Chan School of Public Health, the Harvard Chan Bioinformatics Core (HBC) is a central resource for bioinformatics research, services and training at Harvard and across the Boston biomedical community. We work closely with biomedical scientists to develop and execute innovative workflows to analyze, interpret, visualize and distribute scientific discoveries derived from the analysis of high-throughput data.
Our training team is tasked with the mission of educating the Harvard community on best practices in experimental design, analysis workflows, and data management. Workshops are designed to foster independence and confidence among participants, many of whom are wet-lab biologists applying computational methods to their research for the first time.
The HBC also provides consulting services for researchers wishing to analyze high-throughput biomedical datasets. We work closely with investigators across a broad range of disciplines from the Harvard and with industry collaborators. Projects range from short-term projects (weeks to months) to longer term collaborations (months to years) involving large data sets or multiple omics data types. By combining training and consulting activities, our training team members continue to develop their expertise in NGS data analysis through access to real-world data sets and opportunities to apply emerging and best practice methods.
As a team, we embrace an open source approach to computing and training. We use and teach open source tools and contribute to the development of open source projects; our training materials are freely available on GitHub (https://hbctraining.github.io/main). In line with this community approach, the HBC emphasizes teamwork, collaboration and provides a supportive environment where team members can learn from each other.
You have a background in cancer biology, biomedical or quantitative science and a strong interest in helping biomedical researchers. You have experience with NGS data analysis and enjoy teaching. You thrive on scientific challenges, love sharing knowledge, and enjoy working both collaboratively and independently. You excel at communicating with programmers and wet-lab scientists alike, and are able to explain complex concepts simply in written and spoken form. You are motivated to continually expand your skills and are keen to learn and apply new methods. You have a system for writing good code and managing your data, and see value in enabling reproducible research. You are organized, have strong time management skills and are capable of simultaneously working on multiple projects and meeting deadlines.
The majority of your time will be dedicated to your role as a member of the training team, led by the HBC Training Director. In this role, you will help teach workshops and courses geared towards graduate students, postdocs, research staff and faculty from the greater Harvard community. Training topics include basic data skills (shell, version control, R, high-performance computing, Python, etc.), data management, and NGS data analysis (RNA-seq, ChIP-seq, variant calling, etc.). In addition to teaching existing content at workshops, you will generate new content and update existing content based on the latest best practices. You will also assist with organizational tasks related to training activities. You will be fully involved in determining the future direction of the training program.
You will also spend time providing analysis support to researchers at Harvard and in the broader Boston biomedical community under the direction of the Core’s Director and Associate Director. You will support selected research projects by analyzing high-throughput data with best practice approaches used in the Core, assessing new methods where appropriate. You will document all work thoroughly, and provide clear, manuscript-level reports of analyses and results. Other duties include data management, participation in Core meetings, and liaising with collaborating researchers. Where appropriate, you will participate in developing manuscripts for publication.
- Doctoral degree in one of the following biomedical research areas:
– Molecular/cellular biology
– Cancer biology
– Computational biology
- At least 5 years of bioinformatics experience (including doctoral work) and at least 2 years since completion of your doctoral degree
- Proven experience with teaching courses/workshops with hands-on coding components
- Excellent written and spoken English
- Programming and data skills (Python, R, shell, version control)
- Working knowledge of biology and genetics
Experience in at least one of the following NGS domains:
- Whole genome sequencing
- RNA-seq (bulk/single-cell)
- ChIP-seq or ATAC-seq
- Other sequencing applications
- Proven ability to interpret and analyze large data sets and present results
- Strong interpersonal skills
- Basic knowledge of statistics
- Knowledge of and experience with cancer datasets is a plus
Contact Email firstname.lastname@example.org
Equal Opportunity Employer
We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions or any other characteristic protected by law.
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