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Machine Learning Quantum Computing Jobs in Odenton, MD

QxBranch

Data Scientist QxBranch

Washington, DC · On-site

... potential of Quantum Computing. To make high-stakes, complex business decisions, you must have ... QxBranch is seeking a talented data scientist with a background in machine learning and taking data ...

QxBranch

Data Scientist QxBranch

Washington, DC · On-site

... potential of Quantum Computing. To make high-stakes, complex business decisions, you must have ... QxBranch is seeking a talented data scientist with a background in machine learning and taking data ...

QxBranch

Sales Executive - Analytics

Washington, DC · On-site

... potential of Quantum Computing. To make high-stakes, complex business decisions, you must have ... Understanding of advanced data analytics concepts including machine learning, high performance ...

QxBranch

Sales Executive - Analytics

Washington, DC · On-site

... potential of Quantum Computing. To make high-stakes, complex business decisions, you must have ... Understanding of advanced data analytics concepts including machine learning, high performance ...

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All JobsMachine Learning Quantum Computing JobsMachine Learning Quantum Computing Jobs in Odenton, MD

Machine Learning Quantum Computing information

See Odenton, MD salary details

$28.7K

$47.9K

$99K

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

As of Jul 11, 2026, the average yearly pay for machine learning quantum computing in Odenton, MD is $47,889.00, according to ZipRecruiter salary data. Most workers in this role earn between $36,500.00 and $51,700.00 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.
What cities near Odenton, MD are hiring for Machine Learning Quantum Computing jobs? Cities near Odenton, MD with the most Machine Learning Quantum Computing job openings:
Machine Learning Quantum Computing jobs near you
Postdoctoral Researcher

Postdoctoral Researcher

Johns Hopkins University

Baltimore, MD • On-site

Full-time

Re-posted 20 days ago

Johns Hopkins Medicine rating

7.5

Company rating: 7.5 out of 10

Based on 204 frontline employees who took The Breakroom Quiz

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Job description

Description
The Entropy for Energy (S4E) Laboratory at Johns Hopkins University (PI Corey Oses) has openings for postdoctoral researchers in the data-driven discovery of energy materials. Projects focus on fusion and fission materials, redox chemistry, and quantum-computing-enabled materials discovery.
Qualifications
A. Doctorate in materials science, physics, chemistry, computer science, quantum information science, or related fields.
B. Ability to move to and work at Johns Hopkins University, USA.
C. Proven track-record of first-author publications and presentations. A minimum of three peer-reviewed, first-author publications must be accessible by the S4E team.
D. Proven experience with VASP, Quantum ESPRESSO, LAMMPS, or other ab-initio codes.
E. Strong programming skills in C++ and Python and proficiency with Unix systems.
F. Ability to lead research projects and collaborate with experimentalists.
G. Fundamental understanding of thermodynamics of materials, solid-state physics, inorganic chemistry, and metallurgy at the level of theory/implementation.
H. Expertise in any of the following areas: high-entropy materials, disorder, phonons, magnetism, catalysis, machine learning/artificial intelligence, database/API development, quantum computing for materials science.
Graduate students near the completion of their Ph.D. are welcome to apply. Please provide your anticipated defense date and expected graduation date under Qualification B.
Application Instructions
Submit a single PDF file via Interfolio named "Lastname_Firstname_S4E_202606.pdf" containing a cover letter, CV, and contact information for 3 references. The cover letter should address each point of the qualifications list explicitly (i.e., A, B, C, ...). Provide ample examples and details where possible. Applications without explicit responses will not be reviewed.
OPTIONAL: DOIs for 3 recent and relevant publications can be included at the end of the packet.
Questions can be sent to the S4E team (s4e-admissions at jhu.edu); subject line must contain "S4E Post-Doc".

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