Careers

Available Positions

Our faculty and staff at the Department of Genetics and Genomic Sciences are leading the way in biomedical science and personalized medicine. In addition to the academic and clinical support our faculty and staff receives, you’ll also receive a wide range of personal and professional benefits.

The Center for Transformative Disease Modeling is seeking for highly qualified candidates for a faculty position in computational Neuroscience. The computational neuroscientist is expected to develop cutting-edge data mining and network inference algorithms to analyze large-scale genetic, epigenetic, genomic, and clinical data in neurodegenerative diseases such as Alzheimer's and Parkinson's diseases. This candidate will be responsible for delivery of novel disease biomarkers and targets, biologically plausible disease models for experimental validations.

 Qualifications:

  • PhD in Neuroscience, Computational Neuroscience or a closely related field and 3+ years of experience in these fields.
  • Up-to-date knowledge on signaling pathways and mechanisms in neurodegenerative diseases.
  • Previous experience in target identification or validation in neurodegenerative diseases, especially Alzheimer’s disease.
  • Expertise in cutting edge techniques, particularly single cell sequencing as well as modeling of brain cell communications and brain connectome.
  • Suitable for working in a collaborative multi-disciplinary environment.
  • Strong communication skills, and an ability to collaborate with peers and train postdocs & graduate students effectively are required.

Interested applicants should send Cover Letter, CV, and Research Plan to Professor Bin Zhang at bin.zhang@mssm.edu

The Center for Transformative Disease Modeling is seeking for highly qualified candidates with expertise and strong publication record in the development and/or application of system pharmacology approaches for drug discovery and development. The systems pharmacologist is expected to develop and apply systems and network biology approaches to select druggable targets for diseases like Alzheimer's disease, identify drug candidates, study drug activities (binding and specificity) and generate/optimize drug lead molecules. The candidate will be responsible for developing drug-disease network models and evaluating drug candidates based on ADME-T properties, pharmacological data and therapeutic efficacy. The candidate will collaborate with pre-clinical and clinical teams to ensure transfer of quantitative knowledge to support the design of experimental validations and early clinical studies. 

Qualifications

  • PhD in Pharmacology, Chemistry, Chemical Engineering, Bioinformatics, Computational/Systems Biology, or a related field and a 3+ years of experience in these fields.
  • Proficient in mechanistic modeling using R, Matlab, python or any equivalent software packages is required. Advanced knowledge of AI-based tools to efficiently analyze large datasets is desired.
  • Demonstrated expertise in computational approaches for drug design (e.g. ligand-based and structure-based drug design), especially in the area of quantitative systems pharmacology is required. Expertise with statistics is highly desirable.
  • Proven track records of applying system biology / network methods to issue resolution in drug discovery and developmental challenges and aiding in model-based drug development are required.
  • Expertise in neurodegenerative disease or cancer is also appreciated.
  • Strong communication skills, and an ability to collaborate with peers and train postdocs & graduate students effectively are required.

Interested applicants should send Cover Letter, CV, and Research Plan to Professor Bin Zhang at bin.zhang@mssm.edu

The Bunyavanich Lab is seeking talented individuals who can fulfill the responsibilities and requirements below to apply for post-doctoral positions in our lab.  Our interdisciplinary team led by Professor Supinda Bunyavanich applies epidemiology, multi-omics, and systems biology to interpret multi-scale data generated from human cohorts with asthma and allergic diseases.

Responsibilities:

  • Analyze high-throughput sequence data.
  • Develop and implement methods to analyze these data.
  • Maintain large datasets linked to clinical data.
  • Communicate progress with PI regularly and contribute to the success of the research team.
  • Develop and maintain productive collaborations within Mount Sinai and with outside researchers in academia and industry.
  • Publish and present novel research findings in academic journals and conferences
  • Some supervision of trainees and technical staff may also be required.

Requirements:

  • Degree in bioinformatics, computer science, computational biology, genomics, or a related field.
  • Outstanding programming skills in R, Python, and Unix shell scripting.
  • Excellent track record of analyzing sequence data. Experience with clinical cohorts and microbiome analysis a plus.
  • Demonstrated knowledge of statistics and statistical genetics. Familiarity with genomic data tools, repositories, and databases.
  • Strong attention to detail and solid analytical skills.
  • Ability to work hard and independently while contributing to the team effort and adhering to deadlines.
  • Excellent oral and written communication skills with track record of productive collaborations.
  • Demonstrated ability to work concurrently on several projects, and good understanding of analytic complexities to do independent research as well as assist other researchers.

Interested and qualified candidates may inquire about positions by submitting a CV and detailed letter of interest to Professor Supinda Bunyavanich (Supinda.Bunyavanich@mssm.edu).

The Fang Lab has rich experience in long read sequencing technology (both PacBio and Oxford Nanopore), systems biology and precision medicine. We use long read sequencing to make new discoveries from the human genome, epigenome and transcriptome (Genome Research, 2018; Nature Genetics, 2019). We also pioneered the fast-growing field of bacterial epigenomics (Nature Biotechnology, 2012; Nature Reviews Genetics, 2018, Nature Microbiology, 2020), and the use of DNA methylation for high resolution microbiome analysis (Nature Biotechnology, 2018; Nature Methods, 2021).

Opening

We are looking for highly motivated PostDoc Fellows to 

(1) develop innovative epigenomics and transcriptomics technology using long read sequencing; 

(2) discover novel insights in human diseases; 

(3) translate basic science discoveries into medical impact.

Successful candidates will have unique opportunities with 

(i) a highly interdisciplinary and innovative lab culture where members with dry/wet background closely discuss and learn from each other; 

(ii) our unique expertise in long read sequencing, multi-omics, rich clinical samples and strong collaborators; 

(iii) take the lead role in projects developing cutting edge, pioneering technologies with high biomedical and clinical impact; 

(iv) strong mentorship in both research and career development; 

(v) Compensations are highly competitive with subsidized housing in New York City.

Requirements

1) Candidates with computational and/or experimental background are both welcome. 

2) Ability to learn and master new knowledge and technologies, 

3) Abilities to lead an independent research direction while adapting to a collaborative environment, 

4) An innovative yet critical thinker.

 

How to apply


Please send the following to fanggang@gmail.com: 1) A brief cover letter, 2) CV with a list of publications, 3) PDF files for the papers in which you are first/co-first author.

The Fang Lab has rich experience in long read sequencing technology (both PacBio and Oxford Nanopore), systems biology and precision medicine. We use long read sequencing to make new discoveries from the human genome, epigenome and transcriptome (Genome Research, 2018; Nature Genetics, 2019; Science, 2021 in press). We also pioneered the fast-growing field of bacterial epigenomics (Nature Biotechnology, 2012; Nature Reviews Genetics, 2018, Nature Microbiology, 2020), and the use of DNA methylation for high-resolution microbiome analysis (Nature Biotechnology, 2018; Nature Methods, 2021).

Openings: We are looking for highly motivated Postdoctoral Fellows to:

  1. develop innovative long read sequencing-based methods to study alternative splicing;
  2. make new discoveries in basic biology and translate them into medical impact, in close collaboration with Kristen Brennand Lab @Yale.

Successful candidates will have unique opportunities with: 

  • a highly interdisciplinary and innovative lab culture where members with dry/wet background closely discuss and learn from each other. 
  • our unique expertise in long read sequencing, multi-omics, rich clinical samples and strong collaborators. 
  • take the lead role in projects developing cutting edge, pioneering technologies with high biomedical and clinical impact. 
  • strong mentorship in both research and career development. 
  • Compensations are highly competitive with subsidized housing in New York City.

Requirements

  1. Candidates with computational and/or experimental background are both welcome.
  2. Previous research experience in alternative splicing is required.
  3. Previous experience in long read sequencing is a plus but not required;
  4. Ability to learn and master new knowledge and technologies,
  5. Abilities to lead an independent research direction while adapting to a collaborative environment,
  6. An innovative yet critical thinker.

How to apply: Please send the following to fanggang@gmail.com: 1) A brief cover letter, 2) CV with a list of publications, 3) PDF files for the papers in which you are first/co-first author.