Research Postdoctoral Fellow

Johns Hopkins, founded in 1876, is America's first research
university and home to nine world-class academic divisions working
together as one university.

Salary : $63,480 - $77,076 - Depending on current PGY
level

Johns Hopkins University: School of Medicine: Department of
Dermatology

Description

Position Overview

The Department of Dermatology at the Johns Hopkins University
School of Medicine is seeking a highly motivated postdoctoral
fellow with expertise in machine learning, computer vision, and
image analysis to join a multidisciplinary research program
focused on developing and applying computational approaches to
digital pathology and cancer research.

The successful candidate will work at the intersection of
machine learning, computational imaging, and pathology,
developing novel algorithms to analyze high-dimensional tissue
images and extract quantitative biomarkers of disease response and
tumor biology. The position offers an opportunity to work closely
with pathologists, computational scientists, and clinical
investigators and to contribute to projects spanning translational
research and clinical applications.

Research Focus

The fellow will develop and apply machine learning and
image-analysis approaches to large-scale digital pathology
datasets, with particular emphasis on:

• Development of supervised and deep learning algorithms for

whole-slide image (WSI) analysis, particularly on hematoxylin and
eosin-stained (H&E) slides

• Automated assessment and quantification of tumor

characteristics and tissue classes

• Segmentation, classification, and spatial analysis of tumor and

tissue compartments

• Integration of H&E, immunohistochemistry (IHC), multiplex

immunofluorescence, and other spatial imaging data

• Development of quantitative tissue-based biomarkers for

precision oncology

• Application of machine learning to translational and clinical

research datasets

• Validation and optimization of computational biomarkers across

independent patient cohorts

• Exploration of multimodal approaches integrating pathology

images with clinical and molecular data

The fellow will have opportunities to develop independent research
projects, publish in high-impact scientific journals, present work
at national and international conferences, and collaborate with
investigators across Johns Hopkins and external academic and
industry partners.

Responsibilities

The successful candidate will:

• Develop, implement, and validate machine learning models for

image analysis

• Design computational pipelines for processing and analyzing

digital pathology datasets

• Perform image segmentation, feature extraction, classification,

and spatial analysis

• Develop reproducible computational workflows for large-scale

image analysis

• Collaborate closely with pathologists to translate biological

and clinical questions into computational approaches

• Analyze and interpret complex imaging and clinical

datasets

• Contribute to manuscript preparation, grant applications, and

scientific presentations

• Mentor graduate students and research staff as appropriate

Required Qualifications

• PhD in Machine Learning, Computer Science, Computational

Biology, or a closely related field

• Strong training in machine learning and deep learning, with

hands-on experience training and fine-tuning deep neural networks
(not just using pretrained models)

• Demonstrated experience with image classification and/or

segmentation, ideally in a digital pathology or biomedical imaging

context

• Strong programming skills in Python
• Experience with deep learning frameworks such as PyTorch

(preferred), TensorFlow, or equivalent

• Experience with whole-slide image (WSI) handling (e.g.,

OpenSlide, QuPath, ASAP) and gigapixel-scale image
processing

• Experience with convolutional architectures (e.g., ResNet,

EfficientNet, ConvNeXt) and evaluating/selecting backbones for
downstream tasks such as segmentation

• Experience working with large image datasets and developing

reproducible, version-controlled computational pipelines

• Strong quantitative, analytical, and problem-solving

skills

• Excellent written and verbal communication skills
• Demonstrated ability to work independently and collaboratively

in a multidisciplinary research environment

Preferred Qualifications

Experience in one or more of the following areas is highly
desirable:

• Digital pathology or whole-slide image analysis
• Histopathology or biomedical image analysis
• Cell and tissue segmentation
• Multiplex immunofluorescence or spatial biology
• Weakly supervised, self-supervised, or foundation-model

approaches

• Vision transformers or other modern computer vision

architectures

• Image registration and multimodal image analysis
• Computational oncology
• Experience with high-performance computing or cloud-based

analysis

• Experience publishing machine learning or computational imaging

research in peer-reviewed journals

Research Environment

This position offers an exceptional opportunity to work within the
highly collaborative research environment of the Johns Hopkins
University School of Medicine, with access to extensive
clinical, pathology, imaging, and translational research
resources.

The fellow will work closely with expert pathologists and clinical
investigators while collaborating with computational scientists and
researchers across disciplines. The position is particularly well
suited to an individual interested in developing clinically
meaningful AI and computational pathology approaches that can
ultimately improve cancer diagnosis, treatment assessment, and
personalized patient care .

Qualifications Application Instructions Equal
Employment Opportunity Statement

Salary Range

The referenced salary range represents the minimum and maximum
salaries for this position and is based on Johns Hopkins
University's good faith belief at the time of posting. Not all
candidates will be eligible for the upper end of the salary range.
The actual compensation offered to the selected candidate may vary
and will ultimately depend on multiple factors, which may include
the successful candidate's geographic location, skills, work
experience, internal equity, market conditions, education/training
and other factors, as reasonably determined by the
University.

Total Rewards

Johns Hopkins offers a total rewards package that supports our
employees' health, life, career and retirement. More information
can be found here: https://hr.jhu.edu/benefits-worklife/
.

Equal Opportunity Employer

The Johns Hopkins University is committed to equal opportunity for
its faculty, staff, and students. To that end, the university does
not discriminate on the basis of sex, gender, marital status,
pregnancy, race, color, ethnicity, national origin, age,
disability, religion, sexual orientation, gender identity or
expression, veteran status or other legally protected
characteristics. The University is committed to providing qualified
individuals access to all academic and employment programs,
benefits and activities on the basis of demonstrated ability,
performance and merit without regard to personal factors or
demographic characteristics that are irrelevant to the program
involved.

Pre-Employment Information

If you are interested in applying for employment with Johns Hopkins
University and require special assistance or accommodation during
any part of the pre-employment process, please contact the HR
Business Services Office at jhurecruitment@jhu.edu . For
TTY users, call via Maryland Relay or dial 711. For more
information about workplace accommodations at Johns Hopkins
University for disabilities, medical conditions (including medical
conditions related to pregnancy or childbirth), accessibility, or
religious reasons, please visit accessibility.jhu.edu .

Background Checks

After receiving a conditional offer, the successful candidate(s)
for this position will be subject to a pre-employment background
check including education verification. When deciding whether a
candidate’s conviction history is job-disqualifying, the University
considers the nature and gravity of the offense, the time that has
passed since the conviction, and the nature of the job being
sought.

EEO is the Law

https://www.eeoc.gov/employees-job-applicants

Vaccine Requirements

Johns Hopkins University strongly encourages, but no longer
requires, at least one dose of the COVID-19 vaccine. This change
does not apply to the School of Medicine (SOM). SOM hires must be
fully vaccinated with an FDA COVID-19 vaccination and provide proof
of vaccination status. We still require all faculty, staff, and
students to receive the seasonal
flu vaccine .

Exceptions to the seasonal flu vaccine or COVID-19 vaccine (for
SOM) requirement(s) may be provided to individuals with sincerely
held religious beliefs or medical conditions that preclude them
from receiving the vaccine. Requests for an exception must be
submitted to the JHU vaccination registry. For additional
information, applicants for SOM positions should visit https://www.hopkinsmedicine.org/coronavirus/covid-19-vaccine/
and all other JHU applicants should visit
https://covidinfo.jhu.edu/health-safety/covid-vaccination-information/
.

The following additional vaccine requirements may apply,
depending upon your campus. Please contact the hiring department
for more information.

The pre-employment physical for positions in clinical areas,
laboratories, working with research subjects, or involving
community contact requires documentation of immune status against
Rubella (German measles), Rubeola (Measles), Mumps, Varicella
(chickenpox), Hepatitis B and documentation of having received the
Tdap (Tetanus, diphtheria, pertussis) vaccination. This may include
documentation of having two (2) MMR vaccines; two (2) Varicella
vaccines; or antibody status to these diseases from laboratory
testing. Blood tests for immunities to these diseases are
ordinarily included in the pre-employment physical exam except for
those candidates who provide results of blood tests or immunization
documentation from their own health care providers. Any
vaccinations required for these diseases will be given at no cost
in our Occupational Health office.