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Computational Spatial Transcriptomics Jobs in Peabody, MA

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Computational Spatial Transcriptomics information

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How much do computational spatial transcriptomics jobs pay per hour?

As of Sep 7, 2026, the average hourly pay for computational spatial transcriptomics in Peabody, MA is $60.97, according to ZipRecruiter salary data. Most workers in this role earn between $52.02 and $81.63 per hour, depending on experience, location, and employer.

What is computational spatial transcriptomics?

Computational spatial transcriptomics is a field that combines advanced computational methods with spatial transcriptomics, a technique that measures gene expression within the physical context of tissue samples. It involves processing and analyzing large datasets to map where specific genes are active within tissues, helping researchers understand how cells interact and function in their native environments. This approach is crucial for studies in developmental biology, cancer research, and neuroscience, as it provides insights into cellular organization and tissue architecture. Computational tools help extract meaningful patterns from complex data, enabling discoveries that were previously impossible with traditional methods.

What are some typical challenges faced when working in computational spatial transcriptomics, and how can new team members prepare for them?

Professionals in computational spatial transcriptomics often encounter challenges related to handling and analyzing large, complex datasets that combine spatial and gene expression information. Integrating data from different technologies and ensuring data quality can be demanding, requiring strong programming skills and familiarity with bioinformatics pipelines. New team members can prepare by strengthening their skills in statistical analysis, programming languages like Python or R, and staying updated on the latest spatial transcriptomics techniques. Collaborating closely with experimental biologists and data scientists is also key to overcoming these challenges and driving successful research outcomes.

What are the key skills and qualifications needed to thrive as a computational spatial transcriptomics scientist, and why are they important?

To excel in Computational Spatial Transcriptomics, you need a strong background in bioinformatics, genomics, and statistical data analysis, typically supported by advanced degrees in computational biology or related fields. Familiarity with programming languages (such as R and Python), spatial transcriptomics platforms (like 10x Genomics Visium), and high-throughput sequencing data analysis tools is essential. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for interpreting complex datasets and collaborating with multidisciplinary teams. These competencies ensure accurate data interpretation, innovative research, and successful integration of spatial transcriptomics insights into biological and clinical applications.

What is the difference between Computational Spatial Transcriptomics vs Computational Biologist?

AspectComputational Spatial TranscriptomicsComputational Biologist
Required CredentialsAdvanced degrees in bioinformatics, computational biology, or related fields; experience with spatial data analysisTypically a PhD or Master's in biology, bioinformatics, or related disciplines; strong programming skills
Work EnvironmentResearch labs, biotech companies, academic institutions focusing on spatial genomicsResearch institutions, biotech firms, academia working on biological data analysis
Industry UsageSpecialized in spatial transcriptomics techniques and data interpretationBroad biological data analysis across various fields

Computational Spatial Transcriptomics focuses on analyzing spatial gene expression data within tissues, requiring specialized skills in spatial data processing. In contrast, Computational Biologists work on a wider range of biological data types. While both roles involve bioinformatics expertise, the former emphasizes spatial data analysis techniques specific to transcriptomics.

What job categories do people searching Computational Spatial Transcriptomics jobs in Peabody, MA look for?

The top searched job categories for Computational Spatial Transcriptomics jobs in Peabody, MA are:

What cities near Peabody, MA are hiring for Computational Spatial Transcriptomics jobs?

Cities near Peabody, MA with the most Computational Spatial Transcriptomics job openings:

Infographic showing various Computational Spatial Transcriptomics job openings in Peabody, MA as of August 2026, with employment types broken down into 2% Internship, 58% Full Time, 37% Part Time, 1% Temporary, and 2% Contract. Highlights an 58% Physical, 3% Hybrid, and 39% Remote job distribution, with an average salary of $126,817 per year, or $61 per hour.

Translational Post Doctoral Researcher - Agentic AI for Neurodegeneration

Jj

Cambridge, MA โ€ข On-site, Remote

$126K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 24 days ago


Job description

At Johnson & Johnson,we believe health is everything. Our strength in healthcare innovation empowers us to build aworld where complex diseases are prevented, treated, and cured,where treatments are smarter and less invasive, andsolutions are personal.Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity.Learn more at jnj.com.

As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.

Job Function:

Career Programs

Job Sub Function:

Post Doc - Data Analytics & Computational Sciences

Job Category:

Career Program

All Job Posting Locations:

Cambridge, Massachusetts, United States of America, Raritan, New Jersey, United States of America, San Diego, California, United States of America, Spring House, Pennsylvania, United States of America, Titusville, New Jersey, United States of America

Job Description:

Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow.

Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way.

Learn more athttps://www.jnj.com/innovative-medicine

Johnson & Johnson Innovative Medicine is seeking a Translational Post Doctoral Researcher - Agentic AI for Neurodegeneration for a 2-year fixed term position. This position can be located in either Raritan NJ, Titusville NJ, Spring House PA, San Diego CA or Cambridge MA. (No fully remote option.)

The next frontier in neurodegeneration research is integrating insights across the data we already have at scale with agentic AI in ways which were previously not possible. Whole slide pathology, PET and MRI imaging, multi-omics, and longitudinal clinical records each offer a different lens on the neurodegenerative diseases; brought together, they tell a story no single modality can. This integration challenge is reshaping how we build agentic AI systems for drug discovery and how we evaluate them. Traditional benchmarks were composed for single-modality reasoning. Evaluating whether an AI co-scientist can synthesize across pathology, imaging, molecular, and clinical evidence and produce hypotheses that are biologically sound, demands new frameworks. We are seeking a Postdoctoral Researcher to build them.

The Researcher will be embedded in the Machine Intelligence (MI) team at J&J Innovative Medicine, working in partnership with the c-brAIn academic network. The role begins with engagement in multi-modal neuroscience data - understanding what each modality reveals, how they relate, and where integration breaks down - and builds toward crafting the evaluation frameworks and standards by which agentic co-scientist systems are tested, validated, and trusted.

The Researcher will work day-to-day with AI scientists in J&J's Machine Intelligence group while partnering closely with translational and experimental teams across C-BRAIN's academic network at Washington University in St. Louis and partner institutions. Mentorship is designed to build leaders at the Multi-Modal Data AI Evaluation Neurodegeneration interface, with opportunities for publications, cross-sector exposure, and leadership development.

KEY RESPSONSIBILITIESMulti-Modal Data Integration
  • Characterize and integrate biomedical data modalities - digital pathology (whole slide images), neuroimaging (PET, structural and functional MRI), omics (genomics, transcriptomics, proteomics, metabolomics), and longitudinal clinical data to develop specialized, domain-specific models for neurodegeneration
  • Build and refine data engineering pipelines that harmonize heterogeneous modalities - reconciling differences in spatial resolution, temporal scale, and dimensionality - into unified analytical frameworks
  • Identify where cross-modal integration produces genuine insight versus where it introduces noise or artifact, establishing ground truth for downstream AI evaluation
Agentic AI Evaluation
  • Critically assess AI-driven literature synthesis and automated "third reviewer" capabilities for detecting methodological weaknesses, logical gaps, and unsupported claims across data modalities
  • Establish standards for how agentic systems incorporate overlooked or contradictory evidence such as negative findings, failed clinical trials, etc. and evaluate whether these integrations generate genuinely novel hypotheses
  • Design evaluation frameworks for agentic AI systems operating across neuroscience data modalities - assessing whether models can reason credibly across imaging, omics, and clinical evidence
  • Develop benchmarks using synthetic and real-world multi-modal datasets that probe AI co-scientist capabilities under realistic research conditions, testing for robustness, reproducibility, and alignment with expert-level biomedical reasoning
Research & Communication
  • Serve as a neurodegeneration domain expert within the AI/ML team, ensuring that model outputs remain anchored to clinically relevant disease questions
  • Translate evaluation findings into actionable guidance for AI system development, bridging computational and experimental perspectives
  • Publish evaluation methodologies and findings in leading journals and conferences (e.g., AD/PD, AAIC, NeurIPS)
  • Articulate emerging AI/ML approaches - causal reasoning, intent classification, agentic planning - to diverse audiences with clear framing of practical applications in drug discovery
  • Co-author manuscripts, concept papers, and translational strategy documents
Required Qualifications
  • PhD (or MD/PhD) in neuroscience, neurobiology, computational neuroscience, biomedical informatics, or a closely related field. (*Degree must have been completed within the last 3 years, or will be completed in the next 6 months.)
  • Deep knowledge of neurodegenerative disease biology (Alzheimer's, Parkinson's, etc.) including disease mechanisms, experimental models, and translational challenges
  • Hands-on experience working with at least two of the following data modalities in a research context: neuroimaging (PET, MRI), digital pathology, omics, longitudinal clinical data
  • Familiarity with large language model architectures and agentic AI frameworks (e.g., LangGraph, DSPy, or equivalent orchestration tools)
  • Proficiency in Python and common ML/data engineering frameworks
  • Excellent scientific communication skills and comfort working across computational, translational, and experimental teams
  • Self-directed, with the ability to work both independently and within a diverse, multi-disciplinary team
Preferred Qualifications
  • Experience building data pipelines that integrate heterogeneous biomedical data types
  • Familiarity with evaluation or benchmarking methodologies for AI/ML systems
  • Experience with NLP techniques: named entity recognition, natural language inference, knowledge graph construction
  • Knowledge of graph data structures, graph analytics, and graph platforms (Neo4j, Neptune)
  • Familiarity with cloud infrastructure (AWS and/or Azure) for scalable pipelines

This is a 2-year fixed term position which will be located at one of our offices in either Raritan NJ, Titusville NJ, Spring House PA, Cambridge MA, or San Diego, CA. (NO remote option for this position.)

Johnson & Johnson is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, protected veteran status or other characteristics protected by federal, state or local law. We actively seek qualified candidates who are protected veterans and individuals with disabilities as defined under VEVRAA and Section 503 of the Rehabilitation Act.

Johnson & Johnson is committed to providing an interview process that is inclusive of our applicants' needs. If you are an individual with a disability and would like to request an accommodation, external applicants please contact us viahttps://www.jnj.com/contact-us/careers, internal employees contact AskGS to be directed to your accommodation resource. The anticipated base pay range for this position is $79,000 - $127,650. The Company maintains highly competitive, performance-based compensation programs. Under current guidelines, this position is eligible for an annual performance bonus in accordance with the terms of the applicable plan. The annual performance bonus is a cash bonus intended to provide an incentive to achieve annual targeted results by rewarding for individual and the corporation's performance over a calendar/performance year. Bonuses are awarded at the Company's discretion on an individual basis. Employees and/or eligible dependents may be eligible to participate in the following Company sponsored employee benefit programs: medical, dental, vision, life insurance, short- and long-term disability, business accident insurance, and group legal insurance. Employees may be eligible to participate in the Company's consolidated retirement plan (pension) and savings plan (401(k)).

Employees are eligible for the following time off benefits:
Vacation - up to 120 hours per calendar year
Sick time - up to 40 hours per calendar year
Holiday pay, including Floating Holidays - up to 13 days per calendar year of Work, Personal and Family Time - up to 40 hours per calendar year
Additional information can be found through the link below. https://www.careers.jnj.com/employee-benefits

The compensation and benefits information set forth in this posting applies to candidates hired in the United States. Candidates hired outside the United States will be eligible for compensation and benefits in accordance with their local market.

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Required Skills:

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