Department: Environmental Health & Engineering, Johns Hopkins Bloomberg School of Public Health
Location: Baltimore, MD (on-site with limited hybrid flexibility considered)

About the Role

We are recruiting a highly motivated clinical epidemiology postdoctoral fellow to advance an Exposome Precision Medicine research program with a strong focus on integrating electronic health records (EHR) with geospatial information system (GIS) data and other exposome-related datasets. The fellow will lead analytic pipelines that link longitudinal clinical phenotypes with neighborhood and environmental exposures, wearable or survey-derived measures (when available), and other external data streams to enable patient-level and population-level risk stratification and translational insight.

This position is ideal for a candidate who enjoys building reproducible data infrastructure and translating complex, multi-source data into rigorous epidemiologic analyses that can inform precision prevention and clinical decision support.

Key Responsibilities

EHR extraction, harmonization, and phenotyping

• Extract and harmonize EHR data (diagnoses, procedures, labs, medications, vitals, encounters) for longitudinal cohort construction.
• Develop and validate computable phenotypes for key outcomes (for example cardiometabolic, respiratory, neuroimmune, and multi-morbidity endpoints), including sensitivity analyses across definitions.
• Support mapping to common data models when relevant (for example OMOP), and implement robust QA/QC checks and data provenance.

Geospatial linkage and exposure assignment

• Geocode and manage patient address histories using privacy-preserving methods; create time-resolved residential exposure histories.
• Integrate GIS layers such as air pollution, greenness, heat, built environment, neighborhood deprivation, noise, mobility, and other place-based indicators.
• Implement exposure assignment strategies (buffer-based, grid-based, network-based, and time-weighted approaches) and compare methods for bias and error.

Exposome dataset integration

• Combine GIS-derived exposures with external and internal datasets such as biomonitoring, omics, targeted assays, wearables, and survey instruments when available.
• Build linkable, analysis-ready datasets that preserve temporal alignment between exposures, intermediate biomarkers, and clinical outcomes.

Methods, modeling, and inference

• Apply appropriate designs for longitudinal EHR-linked studies (cohort, nested case-control, case-crossover, target trial emulation where suitable).
• Address confounding, selection bias, and missingness using modern epidemiologic methods (propensity-based methods, marginal structural models, negative controls, multiple imputation).
• Quantify and communicate measurement error and uncertainty in geocoding and exposure assignment.
• Conduct external validation when feasible, and document reproducible pipelines for downstream reuse.

Translation and dissemination

• Produce clear, decision-ready outputs including tables, maps, figures, and model summaries for manuscripts, presentations, and grant proposals.
• Collaborate closely with clinicians, informaticians, and environmental scientists to ensure scientific rigor and translational relevance.

Minimum Qualifications

• PhD (or equivalent) in Epidemiology, Biostatistics, Clinical Informatics, Environmental Health, Population Health, Data Science, or a related field by start date.
• Demonstrated experience working with EHR or administrative health data, including cohort building and phenotype definitions.
• Strong quantitative and programming skills (R and/or Python; SQL strongly preferred).
• Familiarity with at least one GIS/geospatial toolchain (ArcGIS, QGIS, PostGIS, GeoPandas, sf, raster/terra) and spatial linkage methods.
• Excellent scientific writing and communication skills and a strong record of collaborative work.

Preferred Qualifications

• Experience with common data models (OMOP) and/or interoperability standards (FHIR).
• Experience with geocoding workflows and privacy-preserving geospatial analysis for protected health information.
• Knowledge of environmental exposure models and datasets (air pollution surfaces, heat metrics, NDVI/greenness, neighborhood indices, built environment, traffic, noise).
• Skills in causal inference and longitudinal modeling (g-methods, survival analysis, mixed models, target trial emulation).
• Experience integrating multi-modal data (omics, biomonitoring, wearables) with clinical outcomes.
• Familiarity with reproducible data engineering practices (Git, containers, workflow management, documentation, data dictionaries).

Training Environment

Join an interdisciplinary JHU team at the science–policy interface, working with leaders in environmental epidemiology, clinical research, exposomics, and data science. Mentoring emphasizes rigorous study design, reproducible pipelines, and publication-ready outputs, with opportunities to contribute to grant development and cross-cutting initiatives in exposome-driven precision medicine.

Appointment, Compensation & Start

Start date: As soon as possible.
Term: 12-month appointment with expectation of renewal based on performance and funding.
Salary/benefits: Commensurate with experience and aligned with JHU/NIH guidelines.

How to Apply

Email a single PDF to Fenna C.M. Sillé, PhD (fsille1@jhu.edu) including:

1. Cover letter describing fit, relevant experience, and earliest start date;
2. CV (with publications and software outputs/links);
3. Names and contact information for 2–3 referees from different institutions.

Subject line: Postdoc — Exposome Precision Medicine – Clinical Epi.

Commitment to Diversity

Johns Hopkins University is an Affirmative Action/Equal Opportunity Employer and strongly encourages applications from individuals of diverse backgrounds.

Department: Environmental Health & Engineering, Johns Hopkins Bloomberg School of Public Health
Location: Baltimore, MD (on-site with limited hybrid flexibility considered)

About the Role

We are recruiting a highly motivated postdoctoral fellow to translate internal exposome data (multi-omics, clinical labs, inflammatory mediators, extracellular vesicles, and wearables) into production-ready tools for precision medicine. The fellow will help design and validate a mechanism-informed degeneration scoring framework that links biomarkers to cellular injury and tissue dysfunction, and will deploy usable interfaces (APIs/dashboards) for clinical and public-health partners.

Key Responsibilities

Data assembly & harmonization

• Ingest, QC, and harmonize multi-omics/clinical datasets; document metadata and provenance for FAIR reuse.
• Stand up secure, versioned data pipelines (SQL + Python/R) with automated validation.

Modeling & evaluation

• Build reproducible analytics to map biomarkers to hallmarks of cellular injury and tissue decline.
• Perform discrimination/calibration, decision-curve, and fairness assessments; write validation reports.

Productization

• Package scoring and risk-trajectory tools as containerized services (e.g., FastAPI/Plumber) with CI/CD.
• Deliver clinician- and patient-facing dashboards (Dash/Shiny/React) with authentication and logging.

Translation & dissemination

• Collaborate across immunotoxicology, epidemiology, data science, and clinical teams.
• Lead/co-author manuscripts, contribute to grants, and present results to stakeholders.

Minimum Qualifications

• PhD (by start) in Environmental Health, Computational Biology/Bioinformatics, Biostatistics, Computer/Software Engineering, or related field.
• Evidence of production-grade code and maintaining reproducible workflows (Git, tests, CI).
• Proficiency in Python and/or R for multi-omics/clinical analytics; solid SQL and data-engineering skills.
• Experience with at least two: mixtures/EWAS modeling, longitudinal/causal inference, OMOP/FHIR, or geospatial exposure integration.

Preferred Qualifications

• Front-end development for data apps (Dash/Plotly, Shiny, or React/TypeScript).
• Cloud (AWS/Azure/GCP), orchestration (Airflow/Prefect/Snakemake), ML Ops (MLflow/DVC).
• Experience with privacy/IRB/PHI-aware engineering and role-based access control.

Training Environment

Join an interdisciplinary JHU team at the science–policy interface, working with leaders in exposomics, immunotoxicology, and precision health. Mentoring emphasizes rigorous study design, transparent analytics, and effective translation to decision-makers.

Appointment, Compensation & Start

Start date: As soon as possible.
Term: 12-month appointment with expectation of renewal based on performance and funding.
Salary/benefits: Commensurate with experience and aligned with JHU/NIH guidelines.

How to Apply

Email a single PDF to Fenna C.M. Sillé, PhD (fsille1@jhu.edu) including:

• Cover letter describing fit, relevant experience, and earliest start date;
• CV (with publications and software outputs/links);
• Names and contact information for 2–3 referees from different institutions.

Subject line: Postdoc — Exposome Precision Medicine – Computational.

Commitment to Diversity

Johns Hopkins University is an Affirmative Action/Equal Opportunity Employer and strongly encourages applications from individuals of diverse backgrounds.

Department: Environmental Health & Engineering, Johns Hopkins Bloomberg School of Public Health
Affiliation: Center for Alternatives to Animal Testing (CAAT)
Location: Baltimore, MD (hybrid/remote flexibility considered)

About the Role

We seek a postdoctoral fellow to advance methods for assessing the environmental and human-health hazards of biobased solvents. Because these chemistries often fall outside the applicability domains of current models, the fellow will curate high-quality datasets and develop/benchmark computational approaches (read-across, QSAR/QSPR, TTC, and exposure/bioavailability modeling) to better characterize risk and identify key data gaps. Results will directly inform model improvement and regulatory-relevant decision frameworks.

Key Responsibilities

• Data landscape & curation
  • Compile a structured dataset of biobased solvents: structures, physicochemical properties, use categories/production volume, and available hazard/exposure data.
  • Harmonize identifiers (CAS/InChI), normalize units, and document provenance for FAIR reuse.
• Exposure, release & bioavailability
  • Evaluate and run existing exposure/release models; assess fit/applicability for biobased chemicals.
  • Explore partitioning/biotransformation surrogates and simple PBPK/BBDR linkages where justified.
• Hazard prediction & uncertainty
  • Benchmark read-across and QSAR/QSPR methods for sensitization, DNA/protein binding alerts, and TTC assignments.
  • Define applicability domain and quantify uncertainty; propose improvements or model extensions for biobased chemical space.
• Synthesis & translation
  • Map data gaps; recommend targeted testing or NAMs to close high-value uncertainties.
  • Build reproducible, documented pipelines (R/Python + RDKit/cheminformatics stack) and summary dashboards for non-technical stakeholders.
• Dissemination
  • Lead a first-author manuscript comparing biobased vs. conventional solvent chemical spaces and model performance.
  • Present findings at a CAAT meeting focused on regulatory acceptance of read-across; contribute to white papers as needed.

Minimum Qualifications

• PhD in toxicology, environmental health, cheminformatics, computational chemistry, chemical engineering, data science, or related field.
• Hands-on experience with at least two of: read-across, QSAR/QSPR, TTC assignments, exposure modeling, PBPK basics, or AOP-aligned evidence synthesis.
• Proficiency in Python and/or R; and/or machine learning (version control, unit testing, reproducible workflows).
• Strong scientific writing and communication skills.

Preferred Qualifications

• Experience with RDKit or similar toolkits; database design/ETL; ontology/identifier mapping.
• Familiarity with OECD principles for QSAR validation, applicability domain methods, and regulatory contexts for solvents.
• Background in green chemistry/biobased materials, life-cycle thinking, and uncertainty analysis.
• Track record of production-ready analytics (packages, containers, CI/CD) and clear documentation.

Training Environment

You’ll join an interdisciplinary CAAT/JHU team at the science–policy interface, collaborating with experts in computational toxicology, exposure science, green chemistry, and regulatory science. Mentoring emphasizes rigor, transparency, reproducibility, and effective translation to decision-makers.

Appointment, Compensation & Start

• Start date: As soon as possible
• Term: 12-month appointment with the expectation of renewal based on performance and funding.
• Salary/benefits: Commensurate with experience and aligned with JHU/NIH guidelines.

How to Apply

Email a single PDF to:

Fenna C.M. Sillé, PhD, MS

Assistant Professor

Deputy director, Center for Alternatives to Animal Testing (CAAT)

Johns Hopkins University, Bloomberg School of Public Health

Department of Environmental Health & Engineering

CAATjobs@jh.edu

The application should include:

1. Cover letter describing fit, relevant methods, and earliest start date;
2. CV with publications and software outputs;
3. Names/contact info for 2–3 referees from different institutions.
   Subject line: Postdoc — Computational Toxicology (Biobased Solvents).

Commitment to Diversity

Johns Hopkins University is an Affirmative Action/Equal Opportunity Employer and strongly encourages applications from individuals of diverse backgrounds.

Department: Environmental Health & Engineering (EHE), Johns Hopkins Bloomberg School of Public Health
Affiliation: Center for Alternatives to Animal Testing (CAAT)
Location: Baltimore, MD (on-site with limited hybrid work considered)

About the role

We are recruiting a highly motivated postdoctoral fellow to develop data-driven approaches that improve the prediction of off-target toxicity and reactogenicity for vaccine candidates entering first-in-human (FTiH) trials. The fellow will assemble and analyze large repeat-dose toxicity (RDT) datasets from preclinical studies, link them to human clinical safety outcomes, and build interpretable machine-learning models and decision frameworks to inform smarter, faster, and more ethical safety packages. This position is part of a CAAT-led academic–industry collaboration.

The fellow will work closely with faculty in immunotoxicology, regulatory science, and AI/ML, contributing to a meta-analysis platform, curated databases, and prospective modeling tools aligned with 3Rs principles and modern regulatory expectations.

Key Responsibilities

• Data assembly & curation: Aggregate publicly available RDT study data (e.g., SEND-formatted tables) and harmonize endpoints across vaccine platforms (mRNA, adjuvanted proteins), mapping to common ontologies.
• Meta-analysis: Design and execute large-scale analyses to identify preclinical features associated with human reactogenicity, assessing consistency across platforms and encoded antigens.
• Human safety linkage: Collect and standardize adverse event data from established pharmacovigilance sources and compare with preclinical signals to estimate positive/negative predictive value.
• Modeling & tooling: Build reproducible, production-ready pipelines and interpretable ML models for predicting FTiH side-effect profiles; conduct cross-validation and power analyses for prospective use.
• Mechanistic synthesis: Lead a structured literature review on cytokine/immune mediator signatures linked to vaccine side effects; integrate pathway annotations to support causal reasoning.
• Dissemination: Co-author manuscripts, present at scientific meetings, and contribute to white papers/workshops on modernizing vaccine toxicology and reducing animal use.

Minimum Qualifications

• PhD (or equivalent) in toxicology, immunology, microbiology, computational biology, epidemiology/biostatistics, data science, or a related field by start date.
• Demonstrated experience in a) ML/statistical modeling for biomedical data, and b) and large-scale data analysis/meta-analysis
• Strong programming skills (e.g., Python and/or R) and/or machine learning application with clean, reproducible code and version control.
• Excellent written/oral communication and the ability to collaborate across disciplines.

Preferred Qualifications

• Familiarity with preclinical study design and regulatory data standards (e.g., SEND) and/or clinical safety data structures.
• Experience building production-ready analytics (pipelines, dashboards, or packages) used by non-technical stakeholders.
• Background in immunology (cytokines/soluble mediators), vaccinology, or pharmacovigilance.
• Comfort working with sensitive datasets under strict confidentiality.

Training Environment

The fellow will join a collaborative team at CAAT/JHU with strengths in immunotoxicology and population/environmental health, leveraging in vitro and human data to understand how exposures influence immune outcomes across the life course. Mentoring emphasizes rigorous study design, transparent analytics, and high-impact communication.

Appointment, Compensation & Start

• Start date: As soon as possible.
• Term: 12-month appointment with the expectation of renewal based on performance and funding.
• Salary/benefits: Commensurate with experience and aligned with JHU/NIH guidelines.

How to Apply

Please email a single PDF to:

Fenna Sillé, PhD

E-mail: CAATjobs@jhu.edu

Johns Hopkins Bloomberg School of Public Health

Department of Environmental Health & Engineering
615 N. Wolfe St., RM E7628
Baltimore, MD 21205

Application should include:

1. Cover letter describing your fit, relevant experience (research & tooling), and earliest start date;
2. CV with publications; and
3. Names/contact information for 2–3 referees from different institutions.
   Use the subject line: Postdoc – Predictive Toxicology & Vaccine Safety.

Commitment to Diversity Johns Hopkins University is an Affirmative Action/Equal Opportunity Employer and strongly encourages applications from individuals of diverse backgrounds.

Details

• Job Title: Director, Evidence-Based Toxicology Collaboration (EBTC)
• Location: Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (Hybrid Eligible)
• Appointment Type: Non-Tenure Track Faculty Position
• Start Date: As soon as possible
• Website: www.ebtox.org

General Information

The Evidence-Based Toxicology Collaboration is offering a unique and exciting opportunity for someone with talent and drive, perhaps stepping into a first major leadership role, to shape a globally-relevant, forward-looking organization.

This is a full-time, non-tenure faculty position at Johns Hopkins University, leading a voluntary, cross-sector research advocacy group dedicated to advancing evidence-based methods in toxicology and environmental health.

The candidate should have:

• A PhD or equivalent level of experience in research, not necessarily in academic contexts
• Be willing to relocate to or regularly work from Baltimore, to develop close relationships with administrative and research staff at JHU
• Bring experience in fundraising, organizational development, and/or commercial activities, aligned with the ambition of making EBTC a financially independent entity by 2031

The candidate should be an excellent communicator with strong interpersonal skills, be fluent in scientific methods — particularly in experimental and observational research in toxicology or related disciplines — and be committed to the mission, vision, and working principles of EBTC.

More information about EBTC and the role is below.

How to Apply

This position is being recruited via a direct appointment process, managed by the Center for Alternatives to Animal Testing through the Johns Hopkins University Department of Environmental Health and Engineering. The final appointment is contingent on approval of the Committee on Appointments and Promotion and the Dean of the Bloomberg School of Public Health.

To express your interest in being considered for the position, please email by 30 November your curriculum vitae and a maximum one-page cover letter explaining your interest in and suitability for the role to info@ebtox.org. Use the subject line “Application for EBTC Director”.

We will contact a shortlist of the most qualified candidates for informal pre-screening interviews to discuss the role and general strategic direction of EBTC. Formal interviews with the shortlisted candidates will then follow. Unfortunately, we will not be able to contact candidates that do not make the shortlist.

About EBTC

The Evidence-Based Toxicology Collaboration (EBTC) https://www.ebtox.org/ is an international, multi-stakeholder, initiative dedicated to advancing the quality of research and transparency of decision-making in toxicology and environmental health. 

Inspired by the principles of evidence-based medicine, EBTC promotes the adoption of systematic, objective, transparent, and reproducible methods in toxicology, chemical risk assessment, risk-management, and related areas. 

EBTC sits within the Center for Alternatives for Animal Testing under the Department of Environmental Health and Engineering at the Johns Hopkins Bloomberg School of Public Health. EBTC serves as a hub for scientific collaboration, policy engagement, and training in evidence-based methods.

Position Overview

The Evidence-based Toxicology Collaboration (EBTC) at Johns Hopkins Bloomberg School of Public Health is seeking a forward-thinking, mission-driven Director to lead the next phase of its development. 

This non-tenure track faculty position offers a unique opportunity to drive the integration of evidence-based and open science principles into toxicology and chemical safety assessment. 

The Director will serve as the primary strategic and administrative leader of EBTC, with responsibilities spanning research, stakeholder engagement, organizational development, and fundraising.

Key Responsibilities

Fundraising and Community-Building

• Serve as the primary spokesperson for EBTC in national and international venues
• Cultivate funding opportunities from federal agencies, private foundations, and philanthropic donors, nationally and internationally
• Build partnerships with key stakeholders across sectors to expand EBTC’s impact, grow its membership, and develop EBTC’s research and advocacy program

Organisational Development

• Lead the strategic restructuring of EBTC into a financially-independent organisation that can deliver its mission beyond 2031
• Develop a program of revenue-generating and fundable activities alongside the appropriate legal and organisational structures for delivering them

Program Development and Management

• Manage EBTC’s operational functions, including budgeting, staffing, and reporting
• Develop and supervise collaborative projects and working groups involving academic, regulatory, industry, and nonprofit partners

Strategic and Scientific Leadership

• Guide EBTC’s strategic direction and scientific agenda in alignment with its mission
• Coordinate the development of new EBTC research projects and policy initiatives
• Mentor students, postdocs, EBTC members, and staff involved in EBTC-affiliated research

Qualifications

Minimum Qualifications

• Master’s degree in discipline related to toxicology
• 10 years related experience, including demonstrating supervisory or leadership responsibilities
• Demonstrated experience in one or more of coalition-building, research, regulatory science
• Knowledge of “evidence-based” approaches in research, in any discipline
• Demonstrated leadership experience in academic, nonprofit, or scientific collaboration settings
• Experience in fundraising and grant writing

Preferred Qualifications

• PhD or DrPH or equivalent
• Direct experience with policy advocacy; experience in research reform and the science-policy interface is a plus
• Demonstrated leadership experience in collaborative environments, with partnerships with one or more of governmental, industry, non-government, or academic bodies
• Demonstrated experience in securing funding for research or advocacy projects
• Specific knowledge of evidence-based approaches in toxicology and environmental health, and in regulatory science
• Familiarity with processes for establishing new legal entities, especially non-profit, and developing commercial activities that relate to a research program (e.g. consultancy, training)

Appointment and Compensation

This is a full-time, non-tenure track faculty position. Academic rank and salary will be commensurate with experience and qualifications and in accordance with Johns Hopkins University policies.

Benefits: JHU offers an extensive non-salary benefits package.

Salary Range: 85,000 – 120,000 USD