Research Postdoctoral Fellow

Johns Hopkins University; Baltimore (MD); In loco; USD 63.000 - 77.000; Tempo pieno

Descrizione del lavoro

The Johns Hopkins University School of Medicine invites a postdoctoral fellow with expertise in machine learning, computer vision, and image analysis to join a multidisciplinary research program focused on digital pathology and cancer research.

The fellow will develop novel algorithms for whole-slide image analysis, collaborate with pathologists and clinical investigators, and contribute to translational projects that may yield quantitative biomarkers and clinically meaningful AI approaches for

Competenze

• PhD in ML/CS/Computational Biology or related field.
• Strong ML/deep learning background with hands-on model training.
• Experience with image classification/segmentation in pathology.
• Proficient in Python.
• Experience with WSI handling and gigapixel images.

Mansioni

• 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 questions into computational approaches.
• Analyze and interpret complex imaging and clinical datasets.
• Contribute to manuscript preparation, grant applications, and presentations.
• Mentor graduate students and research staff as appropriate.

Conoscenze

Python

Deep learning

Image analysis

Research collaboration

Formazione

PhD in Machine Learning, Computer Science, Computational Biology, or a closely related field

Strumenti

OpenSlide

QuPath

ASAP

PyTorch

TensorFlow

Descrizione del lavoro

Salary

$63,480 - $77,076 - Depending on current PGYlevel

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 postdoctoralfellow 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 andeosin-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 within investigators across Johns Hopkins and external academic and industry partners.

Responsibilities

• 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.

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 [e-mail]. 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.

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-vaccine/ and all other JHU applicants should visit https://covidinfo.jhu.edu/health-safety/covid-vaccination-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.