Computational Biology Postdoctoral Fellow

Boston University; Boston (MA); Presencial; USD 60.000 - 70.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Conference travel funding

Computational equipment budget

Descripción de la vacante

The Zhang Lab at Boston University School of Medicine is seeking multiple Computational Biology Postdoctoral Fellows interested in studying immuno-oncology, kidney disease, or aging.

Postdoctoral scholars will lead the development of novel computational and bioinformatics methods and integrate diverse types of high-throughput biological, omics, and biomedical imaging data generated by the lab and its collaborators.

Formación

• PhD required in a quantitative field (computational biology, CS, biostatistics, mathematics).
• Experience with teamwork and group communication.
• Experience with self-guided learning in a rapidly evolving technical environment.
• Experience in kidney disease, melanoma, Alzheimer’s disease, microbiome, or other relevant disease areas preferred.
• Microbiome/metagenomics familiarity (16S rRNA or shotgun data analysis) is a plus.
• Biomedical imaging analysis experience, including histopathology, spatial transcriptomics, and multimodal data integration, is desirable.
• Background in deep learning and mathematical modeling, with familiarity with graph neural networks, diffusion models, flow matching, and optimal transport.

Responsabilidades

• Develop principled methods using deep learning and advanced mathematical modeling to address biological questions.
• Analyze massive functional genomics data at single-cell and population levels.
• Analyze large cohorts of pathological imaging and spatial transcriptomics data.
• Prepare manuscripts and scientific reports.

Conocimientos

Teamwork

Self-guided learning

Deep learning

Computational biology

Bioinformatics

Educación

PhD in Computational Biology, Computer Science, Biostatistics, Mathematics or related quantitative field

Descripción del empleo

The Zhang Lab at Boston University School of Medicine is seeking multiple Computational Biology Postdoctoral Fellows interested in studying immuno-oncology, kidney disease, or aging. Postdoctoral scholars will lead the development of novel computational and bioinformatics methods and integrate diverse types of high-throughput biological, omics, and biomedical imaging data generated by the lab and its collaborators.

The position offers a competitive salary, dedicated funding for computational equipment, and support for attending national and international conferences. The lab provides an interdisciplinary research environment with opportunities to develop innovative computational methods and apply them to important biological and biomedical questions.

Responsibilities

• Develop principled methods, based on deep learning and advanced mathematical modeling techniques to address biological questions.
• Analyze massive functional genomics data at single cell and population levels.
• Analyze large cohorts of pathological imaging and spatial transcriptomics data.
• Prepare manuscripts and scientific reports.

Qualifications

• PhD in Computational Biology, Computer Science, Biostatistics, Mathematics or other related quantitative areas.
• Experience with teamwork and group communication.
• Experience with self-guided learning in a quickly evolving technical environment.
• Experience in kidney disease, melanoma, AD, microbiome, or other relevant disease areas is preferred.

Additional Preferred Qualifications Related to Potential Projects:

• Microbiome and metagenomics: Familiarity with 16S rRNA or shotgun metagenomic data analysis, commonly used microbiome identification and profiling tools, and microbiome metabolic analysis. Experience studying microbiome-host or microbiome-microenvironment interactions is preferred.
• Biomedical imaging analysis: Experience with biomedical image analysis, including histopathology images (e.g., H&E), spatial transcriptomics, image processing, segmentation, and classification. Experience with multimodal data integration, such as integrating imaging and molecular data or combining different imaging modalities, is highly desirable. Experience with image registration and 3D reconstruction is preferred.
• Deep learning and mathematical modeling: Strong background in deep learning and/or mathematical modeling, with familiarity with graph neural networks, diffusion models, flow matching, and optimal transport.