Postdoctoral Fellow-Computational Biologist

Harvard University; Cambridge (MA); Vor Ort; USD 68.000 - 80.000; Vollzeit

Zusammenfassung

Harvard University’s Arlotta lab invites applications for a Postdoctoral Fellow in Computational Biology. The role centers on computational analysis of multimodal single-cell datasets and collaboration with neuroscience and AI groups to model brain development and neurodevelopmental disorders.

The candidate will work closely with wet-lab scientists, implement advanced analyses (RNA-seq, ATAC-seq, spatial transcriptomics), and contribute to model development using deep learning frameworks.

Qualifikationen

• Ph.D. degree in a relevant scientific discipline or equivalent experience.
• Experience with statistical analysis of omics data and single-cell methodologies.
• Proficiency in Linux/Unix-based HPC and scripting.

Aufgaben

• Analyze large single-cell datasets to define transcriptomic/epigenetic states in brain models.
• Collaborate with wet-lab biologists to design experiments and validate predictions.
• Develop and maintain computational tools for data analysis and modelling.
• Prepare datasets for modelling and integrative analyses with AI/ML models.
• Present results at team meetings and scientific conferences.

Kenntnisse

Statistical analysis

Python/PyTorch

Single-cell data analysis

Linux/Unix HPC

Collaborative teamwork

Ausbildung

PhD in Machine Learning, Computer Science, Bioinformatics, Statistics, Biology

Tools

Python

R

SLURM or qsub

Cloud computing (AWS/GCP)

Jobbeschreibung

Harvard UniversityPosition Details TitlePostdoctoral Fellow-Computational BiologistSchoolFaculty of Arts and SciencesDepartment/AreaStem Cell and Regenerative BiologyPosition 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. Work with wet-lab biologists to design and implement appropriate experiments for collaborative work on model training, validation, and follow-up testing of predictions. Explore advancing technologies such as combined single-cell ATAC and RNA multiomics, pooled CRISPR screening, and spatial transcriptomics. Develop, apply, document, and maintain computational tools, both for own use and to support analysis by biologist colleagues without formal computational training. Work with members of collaborating groups to select and prepare datasets for use in modelling, and define data needs; track, curate, and upload raw and processed datasets to hubs and repositories as appropriate. Follow relevant scientific literature to ensure use of optimal methods and understand emerging practices across the field. Contribute to reports and papers for presentation and publication and present at scientific conferences, as appropriate. Regularly attend and present results at team meetings to share results, plan projects and experiments. Work with other computational biologists experienced with various sequencing technologies, including those at the Stanley Center at the Broad Institute, to learn, discuss, and integrate the most appropriate solution for an experiment or project.

ABOUT THE LABWe encourage applicants to read more about our scientific goals on our lab website (https://hscrb.harvard.edu/labs/arlotta-lab/). In addition, the Arlotta lab is actively working to foster an equitable and inclusive community.

Basic Qualifications Ph.D. degree in Machine Learning, Computer Science, Bioinformatics, Statistics, Biology, or other relevant scientific discipline, or equivalent experience is required.

Additional Qualifications Experience with and solid understanding of statistical analysis Familiarity with next-generation sequence data analysis tools Basic understanding of molecular biology and next-generation sequencing is highly preferred Experience with single-cell data analysis Proficient in Linux/Unix-based high-performance computing (HPC) environments and job schedulers (e.g., SLURM or qsub); experience with cloud computing platforms (e.g., AWS, GCP, Terra) is a plus Experience developing and training deep learning models, ideally with fluency in PyTorch or an equivalent framework, is a plus Experience with one or more of representation learning, generative modeling, graph neural networks, transformers, or foundation model pretraining and fine-tuning is a plus Experience with multi-GPU or distributed training is a plus Ability to work independently as well as part of an interdisciplinary team in a fast-paced environment, while making necessary connections with experts in various computational analysis groups Self-starter, highly motivated Excellent communication and interpersonal skills Excellent organization and time management skills

Special Instructions

Salary Range$67,600-$80,000Pay offered to the selected candidate is dependent on factors such as years of experience, training or qualification, field of scholarship, and accomplishments in the field

Minimum Number of References Required

2

Maximum Number of References Allowed

3

Keywords

EEO/Non-Discrimination Commitment StatementHarvard University is committed to equal opportunity and non-discrimination. We seek talent from all parts of society and the world, and we strive to ensure everyone at Harvard thrives. Our differences help our community advance Harvard’s academic purposes.Harvard has an equal employment opportunity policy that outlines our commitment to prohibiting discrimination on the basis of race, ethnicity, color, national origin, sex, sexual orientation, gender identity, veteran status, religion, disability, or any other characteristic protected by law or identified in the university’s non-discrimination policy. Harvard’s equal employment opportunity policy and non-discrimination policy help all community members participate fully in work and campus life free from harassment and discrimination.

Supplemental Questions

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Applicant Documents

Required Documents Curriculum Vitae Statement of Research Optional Documents Cover Letter Publication OtherPI287382164