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How much do postdoctoral computational drug discovery jobs pay per year?

As of Aug 20, 2026, the average yearly pay for postdoctoral computational drug discovery in the United States is $59,022.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,000.00 and $66,500.00 per year, depending on experience, location, and employer.

What is a postdoctoral computational drug discovery researcher?

A Postdoctoral Computational Drug Discovery researcher is a scientist with a doctoral degree who applies computational methods and modeling to identify, design, and optimize potential drug candidates. They use techniques such as molecular docking, virtual screening, and machine learning to predict how compounds will interact with biological targets. Their work accelerates the drug discovery process by helping to focus laboratory experiments on the most promising leads. These researchers often collaborate with experimental scientists and contribute to publications and grant proposals.

What are some typical collaborative projects a postdoctoral computational drug discovery researcher might work on?

As a Postdoctoral Computational Drug Discovery researcher, you will often collaborate with interdisciplinary teams that include medicinal chemists, biologists, and data scientists. Projects typically involve integrating computational modeling with experimental data to identify and optimize potential drug candidates. You may work on tasks such as virtual screening, molecular dynamics simulations, and structure-based drug design, frequently presenting findings in group meetings and co-authoring publications. This collaborative environment fosters both scientific innovation and professional growth.

What are the key skills and qualifications needed to thrive as a postdoctoral computational drug discovery scientist, and why are they important?

To thrive as a Postdoctoral Computational Drug Discovery scientist, you need a strong background in computational biology, chemistry, or bioinformatics, usually supported by a PhD in a relevant field. Expertise in molecular modeling software, scripting languages (such as Python or R), and familiarity with drug discovery databases and high-performance computing are commonly required. Analytical thinking, problem-solving, and effective communication are vital soft skills for interdisciplinary collaboration and presenting complex findings. These skills are crucial to efficiently design and analyze experiments, accelerate drug discovery pipelines, and contribute valuable insights in a competitive research environment.
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Postdoctoral Computational Drug Discovery jobs near you
Infographic showing various Postdoctoral Computational Drug Discovery job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 20% Part Time, 2% Contract, and 1% Nights. Highlights an 96% Physical, 1% Hybrid, and 3% Remote job distribution, with an average salary of $59,022 per year, or $28.4 per hour.

Post Doctoral Associate

University of Pittsburgh

Pittsburgh, PA

Full-time

Re-posted 10 days ago

Job description

Postdoctoral Associate 

Molecular/structural Biology, GPCR Biology, Organoids, RNA-seq, and AI ML /DL-driven Drug Discovery 

Recruitment focus: molecular biology, structural pharmacology by cryo-EM and X-ray crystallography, baculovirus/Sf9 expression of GPCRs, RNA-sequencing, kidney organoids, SQSTM1/p62-targeted drug MOA studies in multiple myeloma, cannabinoid receptor CB2 pharmacology, and AI/ML/DL approaches. 

Position Overview 

The Xie Laboratory at the University of Pittsburgh School of Pharmacy seeks a highly motivated Postdoctoral Associate or Lab Researcher (PhD or equivalent; exceptional MS-level candidates with relevant experience may also be considered) to join a multidisciplinary research program integrating molecular biology, structural pharmacology, receptor biology, organoid models, RNA-sequencing, and AI/ML-enabled drug discovery. The successful candidate will help advance the lab's programs centered on cannabinoid receptor CB2 signaling and pharmacology, GPCR structural biology, SQSTM1/p62-targeted small-molecule drug mechanism-of-action studies for multiple myeloma, and kidney organoid models for translational research. 

The laboratory builds on its published work in the 3D cryo-EM structure of the human CB2-Gi signaling complex (Cell, 2020; PMID: 32004460) and generative AI modeling for drug design and discovery (Cells, 2022; PMID: 35269537). More information about the lab is available at http://www.cbligand.org/XieLab. CBIDDell22.pharmacy.pitt.edu

Ideal Candidate Profile (in one of the fields) 

         Strong training in molecular biology, biochemistry, biophysics, pharmacology, structural biology, bioinformatics, computational biology, AI/data science, or a closely related field. 

         Hands-on experience with GPCR biology, receptor pharmacology, protein expression & purification, and cell-based functional assays is highly desirable. 

         Experience in human organoid and disease-model research, for example in kidney or oncology organoid model development and translational pharmacology. 

         Experience with cryo-electron microscopy, X-ray crystallography, or integrative structural pharmacology approaches is strongly preferred. 

         Experience with baculovirus/Sf9 expression systems for GPCRs or other membrane proteins is a major advantage. 

         Interest or experience in RNA-sequencing, single-cell or bulk transcriptomics, pathway analysis, and bioinformatics-driven mechanism-of-action studies is desirable. 

         Interest or experience in human kidney organoids, disease modeling, fibrosis biology, or target-specific drug MOA validation is a plus. 

         Ability to work independently while contributing effectively to a multidisciplinary team spanning experimental biology, computational modeling, and translational drug discovery. 

Key Responsibilities 

         Molecular biology and cell signaling: Design and execute experiments involving cloning, site-directed mutagenesis, CRISPR/Cas9-based approaches, receptor signaling assays, binding assays, and cellular functional assays. 

         Structural pharmacology: Support structural studies of GPCRs and signaling complexes using cryo-EM, X-ray crystallography, biochemical characterization, and structure-guided pharmacology. 

         GPCR expression and purification: Develop and optimize baculovirus/Sf9 expression, membrane protein production, purification workflows, and quality-control assays for cannabinoid receptor CB2 and related GPCR targets. 

         Human organoid and disease-model research: Contribute to kidney or oncology organoid model development and target-specific drug MOA studies relevant to fibrosis oncology translational pharmacology. 

         Hematological malignancy drug MOA: Investigate SQSTM-1/p62-targeted small-molecule or antibody autophagy cell data, decision and cell signaling studies for mechanisms of action in acute myeloid leukemia (AML), multiple myeloma (MM), kidney fibrosis, Alzheimer's disease, hematopoietic stem cells, and osteoporosis by using molecular, cellular, and omics-based approaches. 

         RNA-seq and bioinformatics: Generate, analyze, and interpret bulk, single-cell, single-nucleus, or network RNA-seq data to define drug response, pathway modulation, and disease-relevant mechanisms. 

         AI/ML/DL and computational drug discovery: Collaborate on AI, machine learning, deep learning, chemogenomics, graph neural networks, generative modeling, and structure-based drug-discovery workflows. 

         Candidates with Web/mobile app development, data analytics, network information security, GUI design, proficiency in at least one object-oriented programming language (e.g., Java, C , or C#), and scripting language (e.g., Python, PHP) will also be considered 

         Communication and mentorship: Prepare manuscripts, grant materials, presentations, and research reports; maintain organized records; and assist the PI in mentoring graduate students and junior lab researchers. 

Required and Preferred Qualifications 

         PhD or equivalent experience in molecular biology, biochemistry, biophysics, pharmacology, structural biology, bioinformatics, computational biology, AI/data science, computer science, or a related field. 

         Demonstrated ability to conduct independent and innovative research, analyze data rigorously, and communicate findings clearly in written and oral formats. 

         Strong scientific writing skills, attention to detail, and ability to prepare manuscripts, grant-related materials, and technical reports. 

         Experience with BSL2 or BSL2 cell culture, receptor pharmacology assays, protein-expression systems, or omics data analysis is preferred. 

         Experience with Python, R, molecular modeling, structural analysis, or AI/ML/DL workflows is a plus. 

         A collaborative mindset and strong interpersonal skills for working in an interdisciplinary academic and translational research environment. 

Research Areas 

The candidate may contribute to one or more of the following programs: CB2 receptor pharmacology and signaling; GPCR structural biology and ligand discovery; SQSTM1/p62-targeted drug MOA studies for AML and MM/RRMM; kidney organoid model development for drug validation; RNA-seq and pathway analysis; and AI/ML/DL-enabled drug design and translational pharmacology. 

Application Process 

Salary is commensurate with experience. Applicants should apply at www.join.pitt.edu through the Pitt Talent Center for Post-Doctoral Associates, requisition number 26004521 and include: i) a cover letter, CV, and three references; and ii) a summary of past research accomplishments and future research interests/plans.

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