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$31.5K

$74.6K

$109K

How much do inverse imaging problems jobs pay per year?

As of Jun 7, 2026, the average yearly pay for inverse imaging problems in the United States is $74,576.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,000.00 and $83,500.00 per year, depending on experience, location, and employer.

What is the difference between Inverse Imaging Problems vs Image Processing Specialist?

AspectInverse Imaging ProblemsImage Processing Specialist
Required CredentialsTypically requires advanced degrees in applied mathematics, physics, or engineeringOften holds degrees in computer science, electrical engineering, or related fields
Work EnvironmentResearch labs, academic institutions, or specialized imaging companiesMedia companies, tech firms, or healthcare imaging departments
Industry UsageFocused on developing algorithms to reconstruct images from indirect or incomplete dataDesigning and implementing image enhancement, editing, and analysis techniques

Inverse Imaging Problems involve reconstructing images from incomplete or indirect data using complex algorithms, often in research settings. Image Processing Specialists focus on improving and analyzing images through various techniques in commercial or clinical environments. While both roles work with images, their goals, methods, and industries differ significantly.

What are inverse imaging problems?

Inverse imaging problems are a class of mathematical and computational challenges where the goal is to reconstruct an image or signal from incomplete, indirect, or noisy measurements. Common examples include medical imaging (like MRI or CT scans), astronomy, and remote sensing. These problems are 'inverse' because they seek to reverse the process that caused the observed data, often requiring sophisticated algorithms to produce accurate and meaningful reconstructions. Solving inverse imaging problems is crucial for improving image quality and extracting useful information from limited data.

What are the key skills and qualifications needed to thrive as an Inverse Imaging Problems Researcher, and why are they important?

To thrive as an Inverse Imaging Problems Researcher, you need a strong background in mathematics, signal processing, and computational imaging, typically supported by an advanced degree in applied mathematics, electrical engineering, or computer science. Familiarity with programming languages such as Python or MATLAB, as well as experience using machine learning frameworks and optimization toolkits, is essential. Analytical thinking, problem-solving ability, and effective communication are valuable soft skills for collaborating on interdisciplinary research and presenting findings. These skills are crucial for developing innovative solutions to reconstruct images from incomplete or indirect data, advancing both scientific understanding and practical applications.

What are some common challenges faced when working on inverse imaging problems in a professional setting?

Professionals working on inverse imaging problems often encounter challenges such as dealing with noisy or incomplete data, selecting appropriate regularization techniques, and ensuring computational efficiency for large-scale problems. Collaboration with domain experts, such as radiologists or engineers, is crucial to properly interpret results and tailor solutions to specific application needs. Staying updated on advances in optimization algorithms and machine learning methods is also important, as the field evolves rapidly and new tools can significantly improve outcomes.
Antenna Engineering Intern (Fall 2026)

Antenna Engineering Intern (Fall 2026)

Tacit

San Francisco, CA • On-site

$40 - $60/hr

Internship

Posted 24 days ago

Job description

About Tacit
We are an early-stage, deep tech startup based in San Francisco, developing innovative hardware that rethinks human-computer interaction. We are backed by General Catalyst, Khosla Ventures, and Greylock Partners, with a founding team from Stanford, BrainGate, Oculus, and Tesla. While we can't reveal too much just yet, our team is tackling cutting-edge engineering challenges to bring revolutionary products to life.
About the role
We are seeking a highly motivated PhD student for an internship position to help develop and test our wireless sensing hardware. The successful candidate will work closely with our team of engineers and researchers to design, simulate, analyze, tape out, integrate, and validate advanced antenna systems. This role offers hands-on experience across full product lifecycle, with a focus on design optimization and performance reliability. This internship offers a chance to work on hard problems of great impact and a unique opportunity for direct involvement in 0-to-1 product development within a dynamic and collaborative startup environment.
What you'll do
  • Design and evaluation of antennas for a given wireless channel link budget starting from first-principles, while considering pros/cons of different solutions and trade-off between KPIs
  • Perform computational electromagnetic simulations and develop new simulation models using full-wave EM software tools
  • Analyze and interpret simulation results, providing insights and recommendations for design improvements and performance optimization
  • Build mock ups for proof-of-concept prototypes and test vehicles, build test fixtures and platforms, and create solutions to automate the antenna system development process, and data analysis of testing workflows
  • Assist in PCB layout design process, and DFM reviews -considering mass production readiness- with vendors and external manufacturing partners
  • Integrate antennas into complete device form factor by collaborating with cross-functional teams
  • Carry out antenna characterization and ensure that the antennas integrated in our products meet their specifications in free space and on-body from prototype to mass production
  • Assist in sensor calibration to reduce device-to-device variability and maintain reliable performance in different operating conditions and practical user-study cases across user populations
  • Optimize hardware system for low-noise/low-interference, high-resolution signal detection and processing, and ensure compliance with regulatory requirements and power budget
  • Document designs, experiments, and studies; and clearly report findings and recommendations to the wider team
Requirements
  • Currently enrolled in PhD program in Electrical Engineering, Physics, or a related field with a focus on antennas and/or computational electromagnetics.
  • Strong theoretical and practical understanding of antenna theory and electromagnetic wave propagation, as well as basic microwave engineering concepts (e.g. transmission lines, impedance matching, baluns, S-parameters, linear systems, filter design, etc.) • Proficiency in computational electromagnetic simulation tools (e.g., CST, XFDTD).
  • Experience in PCB schematic and layout tools (e.g. Altium Designer, Cadence)
  • Hands-on experience in microwave measurement equipment (vector network analyzers, spectrum analyzers, signal generators, etc.)
  • Hands-on experience designing, prototyping and building functional antenna systems
  • High work ethic, enthusiasm, flexibility, and resourcefulness
  • Excellent problem-solving skills, and ability to work independently and take initiative
  • Strong communication and documentation skills, and ability to work well in a team and across disciplines
Strong candidates may have
  • Experience in on-body or implantable wireless devices, sensing, imaging applications
  • Experience in any of: UWB, directional miniaturized antennas, multiband antennas, flexible electronics, e-textiles, ultra-low power wireless systems, integrated sensing and comms
  • Experience in design optimization and inverse design techniques for electromagnetics structures • Experience in de-sense, coexistence, EMC/EMI and OTA performance
  • Familiarity with AI/ML techniques for signal processing or time-series data (e.g., preprocessing, feature extraction, or integration with deep learning models)
  • Experience with Python or MATLAB for scripting, data processing and test automation
  • Desire for accountability and ownership -able to manage time effectively, drive tasks independently, and escalate and communicate issues and needs clearly
  • Publications or conference presentations in relevant areas
Compensation Range
$40 - $60/hour

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