School: Faculty of Arts and Sciences
Department/Area: Stem Cell and Regenerative Biology
Position Description
The Arlotta lab, in the Department of Stem Cell and Regenerative Biology at Harvard University, is working to identify the mechanisms controlling mammalian brain development and to define cellular and molecular pathways disrupted in brain disorders such autism spectrum disorder and bipolar disorder, by utilizing recent advances in genetics and genomics, and through collaborations with groups using machine learning. We are developing and applying tools to understand how implicated genes act in neurons and circuits. We use large-scale, unbiased, systematic approaches in collaborative multidisciplinary research teams.
This postdoctoral fellowship in the Arlotta lab involves computational analysis of large, multimodal single-cell datasets, as part of a collaborative project which also aims to generate AI and machine learning models of brain development and neurodevelopmental disorders. This person will apply and develop computational methods to analyze datasets of varying modalities, such as RNA-seq, ATAC-seq, and spatial transcriptomics datasets, and of paired molecular and functional measurements (e.g., electrophysiology), as well as optimally leveraging integration with existing genomics datasets.
The role is focused on computational analysis and supporting model development as part of a dynamic, fast-moving experimental program that applies molecular biology to the investigation of brain function and psychiatric illness. This researcher will work in close collaboration with laboratory scientists on a range of projects, with a specific emphasis on the interpretation of molecular profiling results within a biological context, and on interfacing with collaborating groups developing AI and machine learning models using this data. The position entails close collaborations with multiple groups.
CHARACTERISTIC DUTIES
Department/Area: Stem Cell and Regenerative Biology
Position Description
The Arlotta lab, in the Department of Stem Cell and Regenerative Biology at Harvard University, is working to identify the mechanisms controlling mammalian brain development and to define cellular and molecular pathways disrupted in brain disorders such autism spectrum disorder and bipolar disorder, by utilizing recent advances in genetics and genomics, and through collaborations with groups using machine learning. We are developing and applying tools to understand how implicated genes act in neurons and circuits. We use large-scale, unbiased, systematic approaches in collaborative multidisciplinary research teams.
This postdoctoral fellowship in the Arlotta lab involves computational analysis of large, multimodal single-cell datasets, as part of a collaborative project which also aims to generate AI and machine learning models of brain development and neurodevelopmental disorders. This person will apply and develop computational methods to analyze datasets of varying modalities, such as RNA-seq, ATAC-seq, and spatial transcriptomics datasets, and of paired molecular and functional measurements (e.g., electrophysiology), as well as optimally leveraging integration with existing genomics datasets.
The role is focused on computational analysis and supporting model development as part of a dynamic, fast-moving experimental program that applies molecular biology to the investigation of brain function and psychiatric illness. This researcher will work in close collaboration with laboratory scientists on a range of projects, with a specific emphasis on the interpretation of molecular profiling results within a biological context, and on interfacing with collaborating groups developing AI and machine learning models using this data. The position entails close collaborations with multiple groups.
CHARACTERISTIC DUTIES
- Analyze large single-cell molecular profiling datasets to define transcriptomic and epigenetic states in brain organoid models and neuronal cell populations, and interpret results to derive important biological insights into these models.