Pathology Computational Scientist

Applicants should have a high level of interest in:

• A long-term career in an academic medical environment, with the potential for your work to have a direct impact on healthcare outcomes.
• Applying new deep learning technologies to biomedical problems.
• Advancing knowledge of the microbiome and its role in human health and disease.
• Working on an interdisciplinary team and collaborating with computational, wet lab and clinical scientists.
• Engaging with the broader research community to advance applications of AI/deep learning for the microbiome.

About the environment: The Microbiome AI/Deep Learning Lab is an initiative within the Massachusetts Host-Microbiome Center (MHMC) and the Division of Computational Pathology (DCP) at Brigham and Women's Hospital (BWH)/Harvard Medical School (HMS). With recent funding from the Massachusetts Life Sciences Center, the Lab has built a state-of-the-art compute cluster with extensive GPU and CPU nodes, with the objective of making advanced deep learning technologies broadly available to microbiome researchers. The MHMC is a research and core facility that has worked with 100+ groups in the US and internationally to promote understanding of host-microbiome interactions in health and disease, emphasizing a focus on function to define causative effects of the microbiota and to harness this knowledge in developing new therapies, diagnostics and further commercial applications. The DCP is a research division with a broad mandate to develop and apply advanced computational methods for furthering the understanding, diagnosis and treatment of human diseases. BWH is an HMS affiliated teaching hospital, adjacent to the HMS main quad, and the second largest non-university recipient of NIH research funding.

PRINCIPAL DUTIES AND RESPONSIBILITIES:

• Engage in research developing and applying bioinformatic and machine learning approaches, for a variety of microbiology data sources, including next generation sequencing and metabolomic data.
• Substantially contribute to scientific publications and grant applications.
• Analyze datasets and produce visualizations and written reports about scientific findings.
• Deploy computational pipelines on local workstations and on high performance CPU and GPU clusters.
• Organize and curate large datasets using structured approaches, including database systems.
• Engage with trainees and other users of the MHMC to facilitate use of computing resources and application of machine learning technologies for the microbiome.
• Other duties as assigned.

SKILLS/ABILITIES/COMPETENCIES REQUIRED:

• Must be capable of contributing within an interdisciplinary team, exhibit a high level of initiative, and have an eagerness to learn new technologies.
• Ability to manage entire projects in a research environment, from design to implementation, and interpretation of final results.
• Must have experience analyzing microbiome data.
• Must possess advanced knowledge of machine learning, including model development, training, testing and deploying.
• Demonstrated ability to develop and implement computational approaches for analyzing complex biomedical datasets including next generation sequencing data.
• Demonstrated ability to manage large and complex biomedical datasets, using tools such as databases.
• Excellent written and verbal communication skills with demonstrated ability to communicate complex results to both technical and non-technical audiences, through publications and presentations.
• Ability to implement machine learning methods in Python; experience with deep learning and using PyTorch is highly desired.
• Knowledge of software engineering best practices, including source code management/control (e.g., Git) and containerization approaches.
• Experience with high-performance computing environments, including scheduling systems, e.g., SLURM.
• Ability to multitask and prioritize work, to achieve desired goals and deliverables.
• Ability to share expertise, coach, and give general direction to others of different skill sets, backgrounds and levels.