Postdoctoral Research Associate - Data Science for Advanced Manufacturing

We need to output HTML fragment with sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule as needed. Must not repeat job title. Must not include Job ID etc. Must preserve all substantive info, remove boilerplate, navigation, legal, EEO, etc. Must not create sections with placeholder text. Must only include sections where there is real specific content. We need to parse the posting. Key content: Overview: description of role, focus, location, MDF, etc. This could go under "About the role". Major Duties/Responsibilities: list of duties. Basic Qualifications: list. Preferred Qualifications: list. Special Requirements: visa sponsorship not available, export control, PhD within 5 years, appointment length up to 24 months, Real ID, PIV card, background check, etc. Also letters of recommendation requirement. Instructions to upload documents: maybe not needed (application instructions) - we should remove application instructions per rule: "Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions." So we should drop the "Letters Of Recommendation" and "Instructions To Upload Documents" sections? Actually letters of recommendation is a requirement (must submit three letters). That's substantive. The instructions on how to upload is application instruction; we should drop that. Keep the requirement to submit three letters. About ORNL: description of lab, benefits, etc. Benefits section: we have benefits list. Pay: not specified. Schedule: not specified. So we should not create Pay or Schedule sections. We need to include Benefits section if there is real specific content: yes, there is a list of benefits. We need to include Skills? Possibly from qualifications: proficiency in Python etc. Could be under Skills. But we have Requirements (Basic Qualifications) and Preferred Qualifications. Could combine into Requirements and Qualifications? The instruction says use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can have multiple sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits. We need to decide what goes where. We have Basic Qualifications (must have) and Preferred Qualifications (nice to have). Could map Basic Qualifications to Requirements, Preferred Qualifications to Qualifications? Or maybe Requirements = Basic Qualifications, Qualifications = Preferred Qualifications. Skills could be extracted from both (proficiency in Python, etc.) but we could also put them under Skills. We must not repeat content. Let's think: Provide sections: - About the role: overview paragraph(s) describing role, focus, MDF, etc. - Responsibilities: list of duties from Major Duties/Responsibilities. - Requirements: Basic Qualifications (must have). - Qualifications: Preferred Qualifications (nice to have). - Skills: maybe list of technical skills like Python, libraries, etc. But those are already in Requirements. Could avoid duplication by not having Skills if it's redundant. However we could still have Skills summarizing technical proficiencies not covered elsewhere? But we risk duplication. The instruction: "Prefer a few substantive sections over many thin ones." So maybe we skip Skills if content overlaps. - Benefits: list of benefits. We also need to include special requirements (visa sponsorship, export control, PhD within 5 years, appointment length, Real ID, PIV, background check, letters of recommendation). Where to put? Could be under Requirements or a separate section like "Special Requirements". But we only have allowed headings list; we could add a section "Requirements" and include those special requirements there as part of requirements. Or we could add a heading "Requirements" and include both basic qualifications and special requirements. That seems okay. We must not include application instructions like how to upload docs, but we need to keep the requirement to submit three letters of reference. That's a requirement. Thus under Requirements we can list: - Ph.D. in mechanical engineering, material science, electrical engineering, computer engineering, computer science, data science, applied mathematics, or a closely related field - Demonstrated experience with multimodal data acquisition, data analytics, statistical modeling, and machine learning in manufacturing environment. - Proficiency in Python and common data science and machine learning libraries (e.g., NumPy, Pandas, SciPy, scikit-learn, PyTorch, TensorFlow) - Experience developing and deploying machine learning or deep learning models - Ability to present complex results to multidisciplinary teams, including engineering, scientific, and operational stakeholders - Ability to work effectively in a dynamic, collaborative research environment - Excellent verbal and written communication skills - Visa sponsorship is not available for this position. - Export control: position requires access to technology subject to export control requirements; successful candidates must be qualified for such access without an export control license. - Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting their appointment. - Appointment length up to 24 months with potential for extension, subject to performance and funding. - For employment at ORNL, a Real ID compliant form of identification will be required; must obtain and maintain a federal PIV card per HSPD-12 and DOE Order 473.1A, requiring a favorable post-employment background investigation (including declaration of illegal drug activities within last year). - Must submit three letters of reference when applying. We could also include "Letters Of Recommendation" as a bullet. Now Responsibilities: list from Major Duties/Responsibilities: - Develop and integrate imaging and other sensing modalities for data collection and monitoring in manufacturing environment - Develop modular, extensible workflows for data processing - Develop and deploy data analytics, machine learning, and statistical modeling methods for multimodal manufacturing datasets, including sensor streams, in-process signals, post-process characterization data, simulation outputs, and digital twin data. - Develop, integrate, and evaluate AI/ML models for anomaly detection, predictive modeling, process optimization, and automated decision support, including real-time and edge deployment - Collaborate with multidisciplinary teams to provide sensing, computational, and analytical expertise across projects - Support broader research and development activities within the MDF - Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Now About the role: we need to capture overview paragraphs. Overview text: "We are accepting applications for Postdoctoral Research Associate positions in Data Science for Advanced Manufacturing that will focus on the development of next-generation, data-driven manufacturing systems that integrate artificial intelligence, real-time sensing, and digital twins to transform how critical components are designed, produced, and qualified. The selected candidates will conduct research in data science and AI to develop scalable, deployable methodologies to assess and to improve manufacturing quality, efficiency, and certification readiness. This position resides in the Manufacturing Systems Analytics group in the Digital and Secure Manufacturing Section, Manufacturing Science Division, Energy Science and Technology Directorate (ESTD) at Oak Ridge National Laboratory (ORNL). You will work at the MDF to advance digital manufacturing technologies and to accelerate their deployment to industry and national scale applications. The MDF hosts a diverse set of advanced manufacturing systems - including powder bed, directed energy deposition, machining, polymer, and convergent manufacturing systems – used to produce critical components from advanced materials. These systems are instrumented and connected through a unified digital thread platform that captures multimodal, high-frequency data across the full manufacturing lifecycle, from process execution to post-process characterization. This environment enables the creation of high-fidelity digital twins and AI-ready datasets that support real-time monitoring, predictive modeling, and process optimization. In this role, you will leverage large-scale, heterogeneous datasets to develop and deploy AI-driven methods for: Real-time quality monitoring and control of manufacturing processesUnderstanding relationships between manufacturing intent, machine behavior, and part performanceOptimization of manufacturing processes for improved throughput, reliability, and quality You will contribute to the development of integrated data and AI workflows that span data acquisition, modeling, and decision-making, including deployment at the edge and across distributed systems. You will have access to extensive experimental and computational resources and will be expected to publish research, present results, and contribute to high-impact programs. With over 100 manufacturing systems at the MDF, this role offers the opportunity to work on diverse, high-impact problems and to shape the future of intelligent manufacturing." We can condense into a few paragraphs. Now Benefits: list from "ORNL offers competitive pay and benefits programs..." then list: Prescription Drug Plan, Dental Plan, Vision Plan, 401(k) Retirement Plan, Contributory Pension Plan, Life Insurance, Disability Benefits, Generous Vacation and Holidays, Parental Leave, Legal Insurance with Identity Theft Protection, Employee Assistance Plan, Flexible Spending Accounts, Health Savings Accounts, Wellness Programs, Educational Assistance, Relocation Assistance, and Employee Discounts. Also mention medical and retirement plans and flexible work hours, on-site fitness, banking, cafeteria facilities. We should include all. Now we must not include Pay or Schedule sections because not specified. Now ensure we don't include boilerplate like "ORNL is an equal opportunity employer..." etc. That's EEO boilerplate; remove. Also remove "UT-Battelle is an E-Verify employer." etc. Also remove "This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired." That's maybe application instruction; remove. Also remove "We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size." etc. Also remove "Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment." etc. Also remove "If you have trouble applying for a position, please email ORNLRecruiting@ornl.gov." etc. Now produce HTML fragment. We need to use

for section headings. Use

• for any list of duties/requirements/benefits, and

  for prose. Thus:

  About the role

  ... paragraphs ...

  Responsibilities

  • ...
  ...

  Requirements

  • ...
  ...

  Benefits

  • ...
  ...
  We might also include a

  Qualifications? But we already have Requirements covering basic qualifications; we could rename Requirements to "Basic Qualifications" but the instruction says use headings e.g. Requirements, Qualifications. So we could have Requirements for basic, Qualifications for preferred. But we already put special requirements in Requirements; maybe better to split: Requirements = Basic Qualifications + special requirements; Qualifications = Preferred Qualifications. Let's do that. Thus:

  Requirements

  (basic qualifications + special reqs)

  Qualifications

  (preferred qualifications)
  We could also have Skills but not needed. Now ensure we don't duplicate content. Let's extract Preferred Qualifications list: - Experience working with manufacturing, materials, and sensor data - Experience with real-time, time-series or streaming data systems and edge AI deployment - Experience building and maintaining data processing pipelines for structured and unstructured data - Experience with multimodal datasets (e.g., imaging, time-series, and process data) - Experience with API-based data services, workflow automation, or integration of analytics into production systems - Knowledge of experimental design, uncertainty quantification, scientific machine learning, or digital twin methodologies - Experience collaborating across national laboratories, academia, or industry in multidisciplinary teams - Excellent written and oral communication skills (duplicate of basic? but we can keep) - Motivated self-starter with the ability to work independently and to participate creatively in collaborative teams across the laboratory. - Ability to function well in a fast-paced research environment, set priorities