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Machine Learning Quantum Computing Jobs in Pawtucket, RI

Varsity Tutors

Linear Algebra Tutor

Providence, RI · Remote

$18 - $40/hr

... machine learning, and quantum mechanics applications. * Curriculum Awareness & Adaptive Instruction ... vector spaces, computing determinants of large matrices, and grasping the significance of ...

A.I.S., Inc.

Data Scientist

Dartmouth, MA · On-site

Support annotation of datasets and development of machine learning models, including training and ... Familiarity with cloud computing platforms (e.g., Google Cloud Platform or similar) * Experience ...

Varsity Tutors

Python Tutor

Providence, RI · Remote

$18 - $40/hr

Emphasizes readable, maintainable code and connects Python to machine learning, web scraping, scientific computing, and DevOps applications. * Curriculum Awareness & Adaptive Instruction: Familiar ...

Varsity Tutors

Statics Tutor

Providence, RI · Remote

$18 - $40/hr

About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for ... machines, centroids, moments of inertia, friction, and distributed forces. Ability to explain ...

All JobsMachine Learning Quantum Computing JobsMachine Learning Quantum Computing Jobs in Pawtucket, RI

Machine Learning Quantum Computing information

See Pawtucket, RI salary details

$24.8K

$41.4K

$85.6K

How much do machine learning quantum computing jobs pay per year?

As of Aug 2, 2026, the average yearly pay for machine learning quantum computing in Pawtucket, RI is $41,437.00, according to ZipRecruiter salary data. Most workers in this role earn between $31,600.00 and $44,800.00 per year, depending on experience, location, and employer.

What is the difference between Machine Learning Quantum Computing vs Data Scientist?

AspectMachine Learning Quantum ComputingData Scientist
Required CredentialsAdvanced degrees in quantum computing, machine learning, or related fieldsDegree in data science, statistics, or computer science
Work EnvironmentResearch labs, tech companies focusing on quantum tech, academiaBusiness environments, tech companies, consulting firms
Industry UsageEmerging quantum tech industry, research institutionsFinance, healthcare, marketing, e-commerce
Common Search/ComparisonQuantum algorithms, quantum machine learningData analysis, predictive modeling

Machine Learning Quantum Computing specialists focus on developing algorithms that leverage quantum mechanics to enhance machine learning tasks, often requiring advanced knowledge of quantum physics. Data Scientists analyze and interpret large datasets using traditional machine learning techniques. While both roles involve machine learning, the former emphasizes quantum computing applications, whereas the latter centers on data analysis in conventional computing environments.

What are the key skills and qualifications needed to thrive as a Machine Learning Quantum Computing Specialist, and why are they important?

To thrive in Machine Learning Quantum Computing, you need strong foundations in quantum mechanics, linear algebra, and advanced machine learning concepts, typically supported by a degree in physics, computer science, or a related field. Familiarity with quantum programming languages (such as Qiskit or Cirq), cloud-based quantum platforms, and proficiency in Python are usually required, alongside experience with relevant certifications or coursework. Strong problem-solving skills, adaptability, and effective collaboration are vital soft skills in this interdisciplinary field. These competencies are crucial for driving innovation and bridging the gap between quantum computing and practical machine learning applications.

How do professionals in Machine Learning Quantum Computing typically collaborate with interdisciplinary teams?

Professionals in Machine Learning Quantum Computing often work closely with experts in physics, computer science, and engineering. Collaboration usually involves translating quantum concepts for machine learning specialists and vice versa, ensuring that algorithms are both theoretically sound and practically implementable on quantum hardware. Regular meetings, code reviews, and knowledge-sharing sessions are standard, as interdisciplinary insight is crucial for advancing research and developing scalable solutions. Effective communication and a willingness to learn from other domains are essential for success in these teams.

What is Machine Learning Quantum Computing?

Machine Learning Quantum Computing is an interdisciplinary field that combines principles of quantum computing with machine learning techniques. It aims to leverage the computational power of quantum computers to enhance the performance of machine learning algorithms, potentially solving complex problems more efficiently than classical computers. This area includes developing quantum algorithms for tasks such as classification, clustering, and optimization, as well as using machine learning to improve quantum hardware and error correction. Researchers expect that, as quantum hardware matures, this field could revolutionize data analysis, cryptography, and scientific discovery.
Machine Learning Quantum Computing jobs near you
Infographic showing various Machine Learning Quantum Computing job openings in Pawtucket, RI as of July 2026, with employment types broken down into 84% Full Time, 15% Part Time, and 1% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $41,437 per year, or $19.9 per hour.

Chemical Engineering, Environmental Engineering and Materials Science and Engineering Assistant/Asso

Brown University

Providence, RI • On-site

Full-time

Posted 3 days ago

New

Brown University rating

8.1

Company rating: 8.1 out of 10

Based on 27 frontline employees who took The Breakroom Quiz

154th of 614 rated colleges and universities

Job description

Description
The School of Engineering at Brown University invites applications for several full-time faculty positions at the Assistant or Associate Professor level (pre-tenure), with an expected start date of July 1, 2027. We seek visionary researchers and educators in the fields of Chemical Engineering, Environmental Engineering, and Materials Science and Engineering, broadly defined.
Established in 1847, the engineering program at Brown is the oldest in the Ivy League and the third oldest civilian engineering program in the country. Brown Engineering is distinguished by a unique, non-departmental structure designed to break down traditional academic silos, while offering rigorous accredited programs in all major branches of engineering. Successful applicants will build an impactful, independently funded research program that fosters deep interdisciplinary collaboration. We welcome candidates utilizing a broad spectrum of modern research methodologies.
Areas of Interest
Chemical and Environmental Engineering
We welcome applicants working across a wide range of forward-looking areas, with specific interests including, but not limited to:
  • Industrial Decarbonization: Novel pathways for difficult-to-abate sectors such as green cement, low-carbon steel, sustainable hydrocarbon management, metabolic engineering, and synthetic fuel development.
  • Modeling & Systems Integration: Optimization theory, physical modeling, large-scale energy systems integration, computational discovery, atmospheric dynamics, and life-cycle analysis.
  • Separations and Transport Phenomena: Advanced membranes, electrochemical separations, voltage-responsive materials, fate and transport of environmental pollutants.
  • Critical Minerals Recovery: Sustainable extraction, recycling, and processing of elements essential to the global energy transition.
  • Coastal & Climate Resilience: Engineering solutions addressing sea-level rise, infrastructure protection, natural hazards, water sustainability, and ecosystem vulnerability.
  • Environmental Microbiology: microbial fuel cells, biotransformation of emerging contaminants, methanotrophs for climate change mitigation, metagenomics of microbial communities, plant-microbe interactions.
  • Water Treatment Technologies: bioreactors, forward osmosis, nanofiltration, nanosorbents, advanced oxidation processes.

Materials Science and Engineering
We seek candidates pushing the boundaries of structure, properties, and processing of materials, with specific interests including, but not limited to:
  • Quantum Materials & Devices: Design, synthesis, and multiscale modeling of materials for quantum computing, light-driven phenomena, advanced sensing, and solid-state devices,
  • AI-Enabled Materials Discovery & Characterization: Application of machine learning, autonomous workflows, and AI tools for accelerated materials design and automated characterization.
  • Materials for Low-Power Computing: Novel materials systems designed to address the energy demands of next-generation microelectronics and neuromorphic architectures.
  • Mechano-Coupled Phenomena: Interfacial mechanics, chemo-mechanics, and stress-driven behavior in structural or functional materials, especially under extreme environments.

About the Environment
Brown University's School of Engineering provides a unique environment, emphasizing the power of interdisciplinary thought with a focus on expanding connections across engineering disciplines and beyond traditional departmental and school boundaries. The successful candidate will become affiliated with one or more of the interdisciplinary groups within the School of Engineering and contribute to teaching efforts and research across the school, including Chemical Engineering, Environmental Engineering, and Materials Science and Engineering.
Brown University's SoE seeks to recruit and retain a diverse workforce to maintain the excellence of the University and to offer our students richly varied disciplines, perspectives, viewpoints, and ways of knowing and learning.
Review of applications will begin on October 15, 2026 and will continue until the positions are filled.
Equal Opportunity Employer Statement
Brown University provides equal opportunity and prohibits discrimination, harassment and retaliation based upon a person's race, color, religion, sex, age, national or ethnic origin, disability, veteran status, sexual orientation, gender identity, gender expression, or any other characteristic protected under applicable law, in the administration of its policies, programs, and activities. The University recognizes and rewards individuals on the basis of qualifications and performance. The University maintains certain affirmative action programs in compliance with applicable law.
Qualifications
  • Education: A Ph.D. or equivalent in Engineering, Physics, Chemistry, or a closely related discipline is required by the time of appointment.
  • Research Excellence: A strong track record of high-quality peer-reviewed publications, a compelling future research vision, and clear potential for securing external funding.
  • Teaching & Mentorship: A commitment to excellence in undergraduate and graduate teaching, as well as a dedication to mentoring a diverse student body within the classroom and laboratory.

Application Instructions
Candidates should submit the following materials online via Interfolio:
  1. Cover Letter summarizing your qualifications. Please indicate your primary discipline (ChemE, EnvE, MatSci) and your areas of research in your cover letter. Candidates should address how they would contribute to the research and/or teaching missions of our diverse and inclusive university community.
  2. Curriculum Vitae (CV) including a complete publication list.
  3. Research Statement outlining past achievements and future directions (flexible length; typically ~5 pages).
  4. Teaching Statement describing pedagogical philosophy, research mentorship, and experience (typically 1-2 pages).
  5. Letters of Recommendation: Candidates should have at least three letters of recommendation submitted directly with their application, which should speak to the candidate's potential as a scholar, colleague, mentor, and teacher.

Link to apply: https://apply.interfolio.com/190435
Inquiries about the position should be directed to soefa@brown.edu.

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