MSD
Associate Scientist, Postdoctoral Fellow - Integrative Multi-Omics Causal Modeli
MSD, Cambridge, Massachusetts, us, 02140
Postdoctoral Research Fellow – Integrative Multi-Omics Causal Modeling
Our Postdoctoral Research Fellow Program at Merck, Inc. seeks highly motivated and innovative postdoctoral researchers to join the Data, AI and Genome Sciences (DAGS) department to develop and apply cutting‑edge computational and statistical methods for drug target discovery through multi‑omics integration. The role will develop genetics‑informed causal modeling frameworks by integrating human genetics and genomics data with perturbation‑based sequencing datasets to uncover causal gene–pathway–disease relationships.
Key Responsibilities
Develop scalable and robust statistical and computational methods for causal modeling by integrating human genetics, genomics and perturbation datasets.
Build computational pipelines for genetic association analyses, cell‑disease interaction modeling, perturbation‑informed pathway and network inference.
Apply causal inference and network approaches to infer gene→pathway→phenotype relationships to uncover causal molecular mechanisms.
Track cutting‑edge computational and statistical methods in statistical genetics and computational biology, and proactively propose and pilot innovative ideas and approaches.
Collaborate closely with wet‑lab scientists and cross‑functional computational teams.
Communicate findings effectively through publications, presentations, and collaborative meetings.
Qualifications
Ph.D. completed within 6 months of hire in Statistics, Biostatistics, Computer Science, Mathematics, Statistical Genetics, Computational Biology, Bioinformatics, or a related quantitative field.
Demonstrated experience analyzing large‑scale genomics datasets, such as RNA‑seq, single‑cell RNA‑seq, proteomics, etc.
Hands‑on experience applying or developing statistical genetics methods such as GWAS, QTL mapping, variant‑set association tests, polygenic scores, and heritability estimation.
Proficient programming skills in R and/or Python for data analysis, statistical modeling, and pipeline development. Familiarity with cloud platforms or HPC environments, with experience on parallel computing and scalable workflow design.
Familiarity with causal inference methods, network/graphical models, or machine‑learning approaches applied to genomics.
Ability to work independently and collaboratively in a multidisciplinary team environment.
Excellent written and oral communication skills, with a demonstrated ability to publish methodological papers or innovative applications in statistical genetics or computational biology.
Preferred Qualifications
Prior experience with perturbation sequencing assays (CRISPR screens, Perturb‑seq, Drug‑seq) and analysis of perturbation data.
Experience integrating multiple omics data types (e.g., genetics, single‑cell RNA‑seq, etc.).
Experience with Bayesian modeling, graphical models, or causal discovery algorithms.
Experience working with large‑scale biobank (UK Biobank, etc.) or consortium datasets and secure research environments (TREs, DNAnexus, etc.).
Equal Employment Opportunity As an Equal Employment Opportunity Employer, we provide equal opportunities to all employees and applicants for employment and prohibit discrimination on the basis of race, color, age, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or other applicable legally protected characteristics. For more information about personal rights under the U.S. Equal Opportunity Employment laws, visit EEOC Know Your Rights.
#J-18808-Ljbffr
Key Responsibilities
Develop scalable and robust statistical and computational methods for causal modeling by integrating human genetics, genomics and perturbation datasets.
Build computational pipelines for genetic association analyses, cell‑disease interaction modeling, perturbation‑informed pathway and network inference.
Apply causal inference and network approaches to infer gene→pathway→phenotype relationships to uncover causal molecular mechanisms.
Track cutting‑edge computational and statistical methods in statistical genetics and computational biology, and proactively propose and pilot innovative ideas and approaches.
Collaborate closely with wet‑lab scientists and cross‑functional computational teams.
Communicate findings effectively through publications, presentations, and collaborative meetings.
Qualifications
Ph.D. completed within 6 months of hire in Statistics, Biostatistics, Computer Science, Mathematics, Statistical Genetics, Computational Biology, Bioinformatics, or a related quantitative field.
Demonstrated experience analyzing large‑scale genomics datasets, such as RNA‑seq, single‑cell RNA‑seq, proteomics, etc.
Hands‑on experience applying or developing statistical genetics methods such as GWAS, QTL mapping, variant‑set association tests, polygenic scores, and heritability estimation.
Proficient programming skills in R and/or Python for data analysis, statistical modeling, and pipeline development. Familiarity with cloud platforms or HPC environments, with experience on parallel computing and scalable workflow design.
Familiarity with causal inference methods, network/graphical models, or machine‑learning approaches applied to genomics.
Ability to work independently and collaboratively in a multidisciplinary team environment.
Excellent written and oral communication skills, with a demonstrated ability to publish methodological papers or innovative applications in statistical genetics or computational biology.
Preferred Qualifications
Prior experience with perturbation sequencing assays (CRISPR screens, Perturb‑seq, Drug‑seq) and analysis of perturbation data.
Experience integrating multiple omics data types (e.g., genetics, single‑cell RNA‑seq, etc.).
Experience with Bayesian modeling, graphical models, or causal discovery algorithms.
Experience working with large‑scale biobank (UK Biobank, etc.) or consortium datasets and secure research environments (TREs, DNAnexus, etc.).
Equal Employment Opportunity As an Equal Employment Opportunity Employer, we provide equal opportunities to all employees and applicants for employment and prohibit discrimination on the basis of race, color, age, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or other applicable legally protected characteristics. For more information about personal rights under the U.S. Equal Opportunity Employment laws, visit EEOC Know Your Rights.
#J-18808-Ljbffr