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Machine Learning Quantum Computing Jobs in Oregon

... of computing. Collaborating with cross-functional teams, you will develop methods and tools to ... Extract insights from structured and unstructured data using machine learning, coding techniques ...

Statistics Graduate Level Tutor

OR · Remote

$18 - $40/hr

... machine learning research applications. * Curriculum Awareness & Adaptive Instruction: Familiar ... Adapts instruction using R or Python statistical computing, research paper examples, and proof ...

... computing and peer-to-peer distributed consensus networks. The company builds tools and develops ... Machine learning experience. * Energy systems domain knowledge. Additional Information All your ...

... computing and peer-to-peer distributed consensus networks. The company builds tools and develops ... Machine learning experience. * Energy systems domain knowledge. Qualifications Additional ...

Low-level Performance Optimizations, preferably on GPUs Preferred Qualifications: * 3 years+ High-performance computing (HPC) applications development * 1 year+ Machine learning and deep learning ...

Showing results 41-60

Machine Learning Quantum Computing information

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 are the key skills and qualifications needed to thrive as a machine learning quantum computing specialist?

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 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 popular job titles related to Machine Learning Quantum Computing jobs in Oregon?

For Machine Learning Quantum Computing jobs in Oregon, the most frequently searched job titles are:

What job categories do people searching Machine Learning Quantum Computing jobs in Oregon look for?

The top searched job categories for Machine Learning Quantum Computing jobs in Oregon are:

What cities in Oregon are hiring for Machine Learning Quantum Computing jobs?

Cities in Oregon with the most Machine Learning Quantum Computing job openings:

Yield Development Engineer

INTEL

Vernonia, OR • On-site

Full-time

Medical, Retirement, PTO

Re-posted 22 days ago


Intel rating

8.7

Company rating: 8.7 out of 10

Based on 147 frontline employees who took The Breakroom Quiz

19th of 159 rated electronics manufacturers


Job description

Job Description

Embark with us on a journey of growth and transformation as we create exceptionally engineered technology and bring AI everywhere. As a valued team member, your adaptability and attention to detail will contribute to our drive for results and relentless pursuit of quality, ensuring we meet our customers' needs with precision.

Join us and build on our legacy of innovation and collaboration as we deliver world‑changing technology that improves the life of every person on the planet.

Life at Intel: https://jobs.intel.com/en/life-at-intel

As a Yield Development Engineer, you will play a critical role in advancing Intel's cutting-edge semiconductor technologies. You will be part of a team responsible for driving yield improvement strategies that span the entire lifecycle of a technology node, from research and development through high-volume manufacturing. Your contributions will directly influence Intel's ability to maintain its leadership in the semiconductor industry, ensuring the success of innovative silicon solutions that shape the future of computing. Collaborating with cross-functional teams, you will develop methods and tools to analyze vast data sets, identify yield limiters, and implement process changes that enable Intel to achieve its yield milestones. This role promises an opportunity to shape groundbreaking technologies and generate impactful advancements for the industry and beyond. 

Responsibilities will include but will not be limited to:

  • Perform statistical analysis to construct accurate process development roadmaps and drive technology yield milestones. 
  • Analyze and consolidate diverse data sources to identify defect modes and create yield models. 
  • Develop multivariate algorithms for high-volume data analysis to identify root cause yield limiters. 
  • Extract insights from structured and unstructured data using machine learning, coding techniques, and advanced statistical methods. 
  • Collaborate with process integration, defect engineering, and electrical fault isolation teams to debug yield detractors and propose solutions. 
  • Conduct fault isolation and failure analysis to determine the root cause of yield-impacting issues. 
  • Develop tools and systems that transform manufacturing data into actionable yield improvement strategies. 
  • Work closely with design teams to resolve product-related yield issues and improve manufacturability. 
  • Execute new product introductions and participate in factory task forces to ensure process optimization.

Qualifications

You must possess the below minimum qualifications to be initially considered for this position. Preferred qualifications are in addition to the minimum requirements and are considered a plus factor in identifying top candidates Experience listed below would be obtained through a combination of your schoolwork, classes, research and/or relevant previous job and/or internship experiences.

Minimum Qualifications:

  • Bachelor's degree in Engineering, Physical Sciences, Computer Science, or a related field and 4+ years of experience in semiconductor processing, semiconductor devices, or nanotechnology; OR
  • Master's degree in Engineering, Physical Sciences, Computer Science, or a related field and 3+ years of experience in semiconductor processing, semiconductor devices, or nanotechnology; OR
  • PhD in Engineering, Physical Sciences, Computer Science, or a related field.
  • 1+ years of experience in the following:
    • Proficiency in data analysis systems, and machine learning techniques.
    • Optical probing or metrology.
    • Expertise in Statistical Process Control (SPC) and Design of Experiments (DOE) principles.

Preferred Qualifications:

  • PhD in Engineering, Physical Sciences, Computer Science, or a related field and 2+ years of experience in semiconductor processing, semiconductor devices, or nanotechnology.
  • 1+ years of experience in fault isolation, failure analysis tools, and techniques such as electrical testing, and layout studies.
  • Proven ability to collaborate in cross-functional teams and matrixed organizations.
  • Strong project management skills with demonstrated self-initiative and ability to drive strategic objectives in complex environments.
  • Expertise in semiconductor physics and advanced device interactions modeling.
  • Experience maintaining and troubleshooting semiconductor equipment.
  • Demonstrated ability to develop and present yield strategies to technical and business stakeholders.
  • Proficiency in yield modeling methodologies

Take advantage of this opportunity to join Intel and shape the future of engineering. Apply today to be part of a team that drives industry-leading innovation and transformation.

Interview Tips: https://www.intel.com/content/www/us/en/jobs/hiring.html


Posting Statement
All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.
Benefits
We offer a total compensation package that ranks among the best in the industry. It consists of competitive pay, stock, bonuses, as well as, benefit programs which include health, retirement, and vacation. Find more information about all of our Amazing Benefits here.
Annual Salary Range for jobs which could be performed in the US $148,100.00-$209,100.00
*Salary range dependent on a number of factors including location and experience
Working Model
This role will be eligible for our hybrid work model which allows employees to split their time between working on-site at their assigned Intel site and off-site. * Job posting details (such as work model, location or time type) are subject to change.

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Benefits

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About Intel

Sourced by ZipRecruiter

Intel strives to make every facet of semiconductor manufacturing state-of-the-art -- from semiconductor process development and manufacturing, through yield improvement to packaging, final test and optimization, and world class Supply Chain and facilities support. Employees in the Technology and Manufacturing Group are part of a worldwide network of design, development, manufacturing, and assembly/test facilities, all focused on utilizing the power of Moore's Law to bring smart, connected devices to every person on Earth

Industry

Manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1968