This is a fully remote opportunity for an independent researcher who wants their work to move ... Familiarity with DICOM, CT imaging, radiology, or other medical-data formats and workflows is ...
This is a fully remote opportunity for an independent researcher who wants their work to move ... Familiarity with DICOM, CT imaging, radiology, or other medical-data formats and workflows is ...
Dicom Remote information
What is a DICOM Remote?
A DICOM Remote job typically involves managing, troubleshooting, and supporting the Digital Imaging and Communications in Medicine (DICOM) standard for medical imaging from a remote location. Professionals in this role help healthcare organizations ensure the secure transfer, storage, and retrieval of medical images across different systems. They may work with PACS (Picture Archiving and Communication Systems), assist with integrations, and provide technical support to radiology departments and IT staff. Remote DICOM specialists use secure connections and specialized software to perform their duties without being onsite.
What are the key skills and qualifications needed to thrive as a DICOM Remote specialist?
To thrive as a DICOM Remote Specialist, you need a solid understanding of medical imaging standards (DICOM), network protocols, and troubleshooting skills, usually supported by relevant IT or radiology education. Experience with PACS systems, remote support tools, and certifications like CIIP or CompTIA Network+ are commonly required. Strong problem-solving abilities, clear communication, and customer service orientation help you excel in supporting healthcare providers remotely. These skills ensure the secure and efficient exchange of medical images, minimizing downtime and supporting high-quality patient care.
What are some common challenges faced by professionals working in a DICOM Remote role, and how can they be addressed?
In a DICOM remote role, professionals often encounter challenges related to secure data transmission, troubleshooting connectivity issues, and ensuring compliance with healthcare regulations. Working remotely can sometimes make it harder to quickly resolve technical problems or communicate with on-site staff. To address these challenges, it's important to maintain strong cybersecurity practices, develop effective communication protocols with clinical teams, and stay updated on the latest DICOM standards and telehealth best practices. Regular virtual meetings and thorough documentation can also help bridge any gaps caused by remote work.
What is the difference between Dicom Remote vs Dicom Technician?
| Aspect | Dicom Remote | Dicom Technician |
|---|---|---|
| Certifications | Typically requires DICOM or imaging certifications, some remote-specific training | Requires DICOM certification, radiology or imaging tech license |
| Work Environment | Primarily remote, working with hospitals or imaging centers via telehealth | On-site at hospitals, clinics, or imaging centers |
| Industry Usage | Used in telehealth, teleradiology, remote image management | In-hospital imaging, radiology departments |
While Dicom Remote roles focus on remote image management and telehealth services, Dicom Technicians typically work on-site in medical facilities. Both roles require DICOM knowledge and certifications, but differ mainly in work environment and application.
What are popular job titles related to Dicom Remote jobs in Missouri?
For Dicom Remote jobs in Missouri, the most frequently searched job titles are:
What job categories do people searching Dicom Remote jobs in Missouri look for?
The top searched job categories for Dicom Remote jobs in Missouri are:
What cities in Missouri are hiring for Dicom Remote jobs?
Cities in Missouri with the most Dicom Remote job openings:
Full-time
Medical, Vision
Posted 6 days ago
Job description
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a ML Research Engineer / Scientist based in Netherlands.
Join a research-driven team building next-generation AI models designed to understand complete CT studies rather than isolated findings.
You'll work on foundation models, vision-language learning, and multi-finding detection using medical imaging data at unprecedented scale.
Your research will have a direct path from experimentation to regulatory submissions, hospital deployment, and real-world patient care.
You'll own models and experiments end to end, from developing the initial idea through training, evaluation, calibration, and production readiness.
You'll work alongside experienced ML engineers, software engineers, and fellowship-trained radiologists across multiple clinical specialties.
The role combines deep technical research with practical impact, giving you the opportunity to solve challenging problems in medical AI with exceptionally rich real-world data.
This is a fully remote opportunity for an independent researcher who wants their work to move quickly from the lab into clinical practice.
Accountabilities
- Design, develop, train, and evaluate machine learning models capable of interpreting complete CT studies at the study level.
- Research foundation-model approaches for medical imaging, including 3D and volumetric learning at large scale.
- Develop and investigate vision-language models that connect medical images with the terminology and reporting patterns used by radiologists.
- Build models capable of identifying and prioritizing multiple urgent clinical findings simultaneously while maintaining safe and clinically appropriate operating points.
- Design and execute independent experiments, from hypothesis formation and architecture selection through training, evaluation, and analysis.
- Develop custom architectures, training pipelines, loss functions, and distributed training approaches using modern deep learning frameworks.
- Analyze model performance rigorously and establish reproducible evaluation methodologies suitable for clinically consequential AI systems.
- Work closely with fellowship-trained radiologists to understand clinical requirements, interpret results, and translate research findings into practical model improvements.
- Contribute to models and research that progress toward regulatory submissions, clinical deployment, and real-world patient use.
- Take ownership of research projects end to end and make informed decisions about which experiments and approaches are most likely to deliver meaningful improvements.
- Collaborate with ML and software engineering teams to move successful research from experimentation toward robust, deployable systems.
- Strong practical experience with modern machine learning and deep learning, particularly using PyTorch for custom architectures, training loops, and experimentation.
- Deep understanding of why machine learning architectures, objectives, optimization strategies, and training approaches work, rather than relying solely on existing implementations.
- Demonstrated ability to independently formulate hypotheses, design experiments, interpret results, and iterate toward better models.
- Strong understanding of rigorous experimentation, evaluation, reproducibility, and model validation.
- Experience working with large-scale datasets and distributed training environments is highly valuable.
- A strong interest in solving technically challenging problems where model performance and reliability have meaningful real-world consequences.
- Ability to work effectively with researchers, engineers, and clinical experts in a collaborative environment.
- Medical imaging, 3D computer vision, or volumetric-data experience is advantageous but not required.
- Experience with vision-language models or self-supervised learning is a plus.
- Familiarity with DICOM, CT imaging, radiology, or other medical-data formats and workflows is beneficial.
- A PhD, research publications, or a strong academic research background is a plus, but not a prerequisite.
- Prior medical-AI experience is not required; a willingness to learn clinical concepts directly from radiology experts is valued.
- Strong written and verbal communication skills and the ability to work independently in a fully remote environment.
- Fully remote position open to candidates worldwide.
- Location-flexible compensation with a cash-weighted base salary determined according to the local market in the country where you work.
- Specific compensation range for your location shared early in the hiring process.
- No equity included in international offers, with compensation structured transparently around local-market cash pay.
- Opportunity to work with a real-world CT dataset covering approximately 10 million patients, paired with radiology reports.
- Direct collaboration with fellowship-trained radiologists across areas including chest, body, MSK, neuro, and oncology.
- Opportunity to work on research that can progress from experimentation to FDA submissions, hospital deployments, and patient care within months.
- Exposure to large-scale foundation models, vision-language learning, distributed training, medical imaging, and clinically focused AI evaluation.
- High degree of ownership over research ideas, experiments, models, and technical direction.
- Opportunity to work alongside researchers and engineers with significant contributions to medical AI, open datasets, algorithms, and clinical AI systems.
- A small, research-oriented team where successful ideas can move quickly from research into production.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
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