Bioinformatics of Alzheimer’s

Seeking the next leaders in computational systems medicine – Alzheimer’s Disease

Alzheimer’s therapeutics and diagnostics

A postdoctoral traineeship is offered in diagnostic and therapeutic applications for Alzheimer’s Disease.  The study will exploit single-cell, spatial, and tissue level transcriptomic data coupled with genomic regulatory circuitry and in-depth noncoding RNA data. 

We actively encourage diversity: members of under represented minorities are particularly encouraged to apply.

The traineeship is part of our career development program to rapidly develop molecular models to apply to diagnostics and drug repurposing.

The position is under the direction of Dr. Winston Hide (Systems RNA medicine). The laboratory primarily uses computational modeling of neurodegenerative diseases prior to validating results with collaborators in vitro, and in mouse models. For a list of publications from the Hide lab see here.  


The postdoctoral trainee will: 

  • Generate and analyze small RNA-Seq profiles from exosomes of Alzheimer’s disease patients.  
  • Apprehend, curate, and analyze miRNA expression profiles relating to Alzheimer’s disease (AD) datasets from collaborators and the public domain to evaluate and synthesize molecular signatures for blood-based diagnosis of AD. 
  • Integrate small RNA-Seq results with other available public and in-house data types, including but not limited to RNA-Seq, single cell RNA-Seq, methylation, and spatial transcriptomics.   
  • Develop systems biology models for prioritizing actionable miRNAs for downstream drug screening.  

Required Qualifications 

  • PhD in a quantitative field related to bioinformatics (e.g. genomics, computational biology, biostatistics, or computer science) 
  • Extensive experience working with RNA-seq datasets.  
  • Experience with small RNA-Seq is highly desired.  
  • Experience with single cell and spatial transcriptomics technologies. 
  • Excellent working knowledge of R and other scripting languages 
  • Experience with network modeling and pathway analysis.  
  • Ability to generate computational disease models and hypotheses  
  • Superb communication skills 
  • Ability to work independently and as part of a team 
  • Demonstrated ability to drive a research project from design stages to data analysis, figure preparation and manuscript preparation 
  • A passion for scientific research 
  • Strong organization and time-management skills 
  • Meticulous attention to detail 

This position is a fundamentally important one and we are seeking a highly motivated individual who relishes a challenge and is not shy about diving into complex datasets.  


Additional expertise 

  • Sound knowledge of statistics  
  • Experience with manipulating and curating Alzheimer’s transcriptome datasets 
  • Experience using large-scale datasets to rank gene and pathway candidates, and to define key network events that may be driving a disease process  
  • Extremely comfortable with network-orientated bioinformatics 
  • Knowledge of the aging and the neurodegeneration research field 
  • A strong understanding of genetics 
  • Experience with human-derived model systems 



The position is available immediately and can be renewed annually 

How to Apply 

Email applications including curriculum vitae, a summary statement of personal objectives and research interests, PDFs of your best two papers, and the names and email addresses of three references directly to: whide [at] bidmc [dot] harvard [dot] edu 


About the Hide Lab: 

The Hide lab is based in Beth Israel Deaconess Medical Center/Harvard Medical School and is part of the Harvard Initiative for RNA Medicine and BIDMC Cancer Center. Our projects are funded by NIH, Harvard Medical School, and the Cure Alzheimer’s fund. We are a part of CIRCUITS consortium within CureAD foundation. Our collaboration extends to several US institutions –such as HMS, Mass General Hospital, and MIT—and international institutions.  

As a member of Hide Lab you will: 

  • Work on cutting-edge research in computational and systems biology with access to cutting-edge Alzheimer’s disease omics data.  
  • Collaborate with world-class researchers in neurodegenerative diseases and non-coding RNA. 
  • Receive comprehensive, focused, career directed hands-on training to pursue your goals in research and academia including: scientific communication, collaboration, and grant writing. 
  • Benefit from training opportunities offered by Harvard Medical School, the Harvard Catalyst, and BIDMC.  
  • Work in a vibrant and dynamic lab environment with supportive colleagues.  

The position is available immediately and can be renewed annually

How to Apply
Email applications including curriculum vitae, a summary statement of personal objectives and research interests, PDFs of your best two papers, and the names and email addresses of three references directly to: whide [at] bidmc [dot] harvard [dot] edu

Position2: Seeking a skilled bioinformatician who relishes the reality of sequence analysis of WGS for over 3000 subjects with Alzheimer’s

Please apply to whide [at] bidmc [dot] harvard [dot] edu with your cv

As part of the Cure Alzheimer’s Genome Project, the Hide laboratory at Beth Israel Deaconess Medical Center seeks a highly motivated person to join our team. As a Computational Genomics Scientist, you will be responsible for application, benchmarking and continuous improvement of our genome analysis pipeline for whole genome sequencing (WGS) of subjects afflicted with Alzheimer’s. You will manage apprehension, storage, security and delivery of whole genome sequencing data into a cloud environment such as Terra where analysis will take place. You will oversee daily operation of the pipeline and handling of custom analysis, annotation and reporting for BIDMC staff and our collaborators. You should have significant experience in DNA sequencing analysis, including evidence of familiarity with best practice approaches to alignment, variant calling, and structural variant calling. It is important that you have previous experience in cloud-based analyses, preferably at scale. Expertise in Alzheimer’s disease genetics would be ideal. You will be comfortable with statistical methods for data QC and evaluation and presentation of both production metrics and scientific results. Your excellent communication skills and successful collaboration with other members of the computational biology team as well as external collaborators and clients is really important to us. We would welcome your insights and research activity in integrating WGS into omics.

Job duties will include, but are not limited to:

  • Primary responsibility for maintenance, improvement, and operation of the WGS analysis pipeline
  • Remaining current with  and performing analysis of sequencing data using best practice workflows
  • Development of novel sequence analysis methods where off-the-shelf methods are inadequate
  • Outwards community facing interaction to support / drive best practice
  • Generate and track appropriate quality metrics for high throughput sequencing operations
  • Work with internal staff and collaborators to implement methods or prototype and benchmark customized analysis and annotation pipelines
  • Work with the clinical team to manage transfer of research pipelines to the clinical lab as needed
  • Work with the Broad Institute Terra management team to ensure optimal and efficient use of cloud facilities.
  • Assist, collaborate, and consult with internal/external researchers on analysis of genomic data
  • Interpret and present analysis results to co-workers and collaborators
  • Perform research and data integration leveraging the tools and data you generate
  • Publish results in scientific journals and give presentations at conferences when possible.

Required Skills: 

  • Ph.D. in bioinformatics, computational biology, genetics, computer science or similar
  • Experience in DNA sequencing analysis, including alignment, variant calling, structural variant calling, and interactive exploration of variant information and annotation.
  • Experience in population genetics and genetics of complex traits. 
  • Proficiency in utilizing data from public resources such as Genome in a Bottle, 1000 Genomes, ExAC, and GnomAD
  • Proficiency in R, and one or more programming languages such as Python, Perl, Java, or C/C++, willingness to learn new programming languages 
  • Experience working in Linux and running tasks in a cluster environment and experience in cloud environments
  • Experience at minimum with sufficient statistical knowledge to develop and interpret standard QC metrics for sequencing 
  • Cloud data manipulation/analysis/processing
  • Experience working in teams centered around a biological question and with external collaborators
  • Excellent written and verbal communication skills.