Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Micron Technology, Inc. のファブエンジニアとして, 他のプロセスエンジニアや装置エンジニアと協力し, 製品の品質と信頼性を向上させるためのプロセスの立ち上げ, 開発, 最適化を担当します。また, プロセスの歩留まり改善, コスト削減, 生産性向上, リスク管理に取り組むとともに, 製造ライン上で発生する問題の解決することが期待されます。。 As a Fab Engineer at Micron Technology, Inc., you will be working with a team of other process and equipment engineers responsible for starting up, developing and optimizing processes to improve product quality and reliability, working on process yield improvement, cost reduction, productivity improvement and risk management as well as resolving manufacturing line problems. 職務内容 Job Description • プロセス条件および技術を確立し, 改善する。 Establish and improve process condition and technology.• プロセス能力を向上させ, 生産コストを削減する。 Upgrade process capability and reduce production cost.• プロセス管理プロジェクトを立ち上げ, 管理する。 Establish and modify process management projects.• 各種半導体装置のプロセスパラメータを設定する。 Set up process parameters for a variety of semiconductor equipment.• 新規設備• 材料の評価, 導入推進および計画を行う。 Evaluation, promotion and planning of new equipment / materials.• 異常の分析および改善を実施する。 Abnormal analysis and improvement. 要件 Requirement• 分析に基づく問題解決スキルを持っている。P roblem-solving skills based on analytical and logic thinking.• チーム内外の関係者と適切なコミュニケーションが取れ, 連携した業務の遂行が出来る。 Communicate effectively with stakeholders inside and outside the team and perform tasks collaboratively.• テクニカルな話題を理解, 発表, 教育, 開発を行る能力がある。 Capable of understanding, presenting, teaching, and developing technical topics.• 時間管理, マルチタスク能力を持ち合わせている。S trong time-management and multitasking abilities.• Microsoft Office (Excel/Word/Power Point)スキルを有する。 Microsoft Office (Excel, Word, PowerPoint) Skill. About Micron Technology, Inc. We are an industry leader in innovative memory and storage solutions transforming how the world uses information to enrich life for all . With a relentless focus on our customers, technology leadership, and manufacturing and operational excellence, Micron delivers a rich portfolio of high-performance DRAM, NAND, and NOR memory and storage products through our Micron® and Crucial® brands. Every day, the innovations that our people create fuel the data economy, enabling advances in artificial intelligence and 5G applications that unleash opportunities - from the data center to the intelligent edge and across the client and mobile user experience. To learn more, please visit micron.com/careers All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status. To request assistance with the application process and/or for reasonable accommodations, please contact hrsupport_japan@micron.com Micron Prohibits the use of child labor and complies with all applicable laws, rules, regulations, and other international and industry labor standards. Micron does not charge candidates any recruitment fees or unlawfully collect any other payment from candidates as consideration for their employment with Micron. AI alert: Candidates are encouraged to use AI tools to enhance their resume and/or application materials. However, all information provided must be accurate and reflect the candidate's true skills and experiences. Misuse of AI to fabricate or misrepresent qualifications will result in immediate disqualification. Fraud alert: Micron advises job seekers to be cautious of unsolicited job offers and to verify the authenticity of any communication claiming to be from Micron by checking the official Micron careers website in the About Micron Technology, Inc.

Title: Research Associate or Research Scientist - Ag. Economics and Remote Sensing Location: Blacksburg, Virginia - Blacksburg Virginia Department: Agricultural & Applied Economics Job Description: Job Description The Department of Agricultural and Applied Economics at Virginia Tech, and in particular an interdisciplinary research team led by Dr. Elinor Benami, is seeking a highly motivated Research Associate or Scientist to join us in the Summer or Fall 2026. The position is annually renewable pending availability of continued funding. This is a single search for one research faculty position that may be filled at either rank, depending on the qualifications of the finalist candidate. Research Associate conducts research under supervision with more limited independent authority, contributes to manuscripts and reports under guidance, and is generally not eligible to serve as principal investigator. Research Scientist conducts independent research under general supervision, contributes significantly to project conceptualization and management, is expected to lead manuscripts and contribute to proposal and grant development, may supervise project personnel and research assistants, and is eligible to serve as principal investigator. Our team works at the intersection of agricultural economics, remote sensing, and insurance and finance, with projects spanning how the use of Earth observation can inform agricultural index insurance design, premium ratings, and quantifying the private and public values of conservation agricultural practice adoption. We are affiliated with NASA Harvest and collaborate with a wide range of academic, government, and practitioner partners, including USDA, the Gates Foundation, Microsoft AI for Good, and multiple public, private, and nongovernmental organizations. The successful candidate will be responsible for supporting the research efforts of our team using a variety of tools and methods. These will include preparing geospatial and administrative data for analysis, applying theory-guided statistical approaches to uncover relationships in the data, generating visualizations and maps, preparing manuscripts for submission to peer-reviewed journals, and presenting findings to academic as well as practitioner audiences. The candidate will also have opportunities to engage directly with policy and industry partners and to contribute to ongoing grant development. We are especially interested in candidates with a background in agricultural or applied economics/finance and familiarity with remote sensing data and methods, though we welcome applicants from related quantitative disciplines, including economics, earth system or environmental sciences, computational social science, and geography, provided they bring strong causal inference skills and engagement with applied policy questions. Experience with land markets, agricultural finance, or soil and water conservation policy is a plus. The position is best suited to someone who is comfortable working across disciplinary boundaries, interested in both the scholarly and applied dimensions of the work, and eager to contribute to a collaborative and fast-moving research environment. Location: Blacksburg, Virginia, with travel to partner institutions domestically or internationally occasionally expected. Some remote work is permissible by mutual agreement. About NASA Harvest | NASA Harvest is NASA’s Global Food Security and Agriculture Program. Our mission is to enable and advance adoption of satellite Earth observations by public and private organizations to benefit food security, sustainability, agriculture, and human and environmental resiliency worldwide. We accomplish this through a multidisciplinary and multisectoral Consortium of leading scientists and agricultural stakeholders, led by researchers at the University of Maryland and implemented through co-development with our partners across the globe. About Virginia Tech | Virginia Tech is and has been pursuing a series of cluster hires to complement the strengths of existing faculty in pursuing interdisciplinary research the nexus of digital, biological, social, and physical sciences and engineering with application to agriculture, food, and natural resources. This vision aims to create a state and nation-wide network of interconnected faculty, partners, and resources for scientific discovery and developing and deploying new technologies. The goal is to increase overall efficiency, resiliency, sustainability, and economic value of food, agriculture production systems, and natural resources while expanding Virginia Tech’s global influence in this rapidly evolving domain. Required Qualifications - Requirements for Research Associate Position - Master's degree in agricultural economics, applied economics, or a related quantitative field, or a bachelor’s degree and significant relevant experience in the same fields - Strong written and oral communication skills - Demonstrated experience with data analysis in R, Python, or similar language using version control ( e.g., Git) - Experience in evaluating the economic and policy implications of agricultural monitoring techniques, ideally using remotely sensed, Earth observation data - Requirements for Research Scientist Position - PhD in agricultural economics, applied economics, or a related quantitative field with relevant research experience - Strong written and oral communication skills - Demonstrated experience with data analysis in R, Python, or similar language using version control ( e.g., Git) - Experience in evaluating the economic and policy implications of agricultural monitoring techniques, ideally using remotely sensed, Earth observation data Preferred Qualifications - Preferred Qualifications for Research Associate Position - Experience with econometric methods applied to administrative or observational data - Familiarity with geospatial analysis and remote sensing data, including cloud platforms such as Google Earth Engine - Prior work in one or more of the team's areas: agricultural/index insurance or risk transfer, conservation agriculture, land markets, or rural finance - Preferred Qualifications for Research Scientist Position - Experience submitting manuscripts to peer reviewed journals and established publications record - Experience with managing sponsored research projects and/or personnel - Experience with econometric methods applied to administrative or observational data - Familiarity with geospatial analysis and remote sensing data, including cloud platforms such as Google Earth Engine - Prior work in one or more of the team's areas: agricultural/index insurance or risk transfer, conservation agriculture, land markets, or rural finance Overtime Status Exempt: Not eligible for overtime Appointment Type Restricted Salary Information 57,783 - 89,412 (dependent upon rank) Hours per week 40; M-F 8:00 a.m. - 5:00 p.m. Review Date 07/17/2026 Additional Information To apply: Please submit (1) a cover letter describing your interests and relevant background, (2) your CV, and (3) a writing sample or link to a coding portfolio (e.g., GitHub profile). Applications will be reviewed on a rolling basis with a priority deadline of July 17, 2026. Contact for questions: Elinor Benami, elinor@vt.edu . The successful candidate will be required to have a criminal conviction check. About Virginia Tech Dedicated to its motto, Ut Prosim (That I May Serve), Virginia Tech pushes the boundaries of knowledge by taking a hands-on, transdisciplinary approach to preparing scholars to be leaders and problem-solvers. A comprehensive land-grant institution that enhances the quality of life in Virginia and throughout the world, Virginia Tech is an inclusive community dedicated to knowledge, discovery, and creativity. The university offers more than 280 majors to a diverse enrollment of more than 36,000 undergraduate, graduate, and professional students in eight undergraduate colleges, a school of medicine, a veterinary medicine college, Graduate School, and Honors College. The university has a significant presence across Virginia, including Blacksburg, the greater Washington, D.C. area, the Health Sciences and Technology Campus in Roanoke, sites in Newport News and Richmond, and numerous Extension offices and research institutes. A leading global research institution, Virginia Tech conducts more than $650 million in research annually. Virginia Tech endorses and encourages participation in professional development opportunities and university shared governance. These valuable contributions to university shared governance provide important representation and perspective, along with opportunities for unique and impactful professional development. Virginia Tech does not discriminate against employees, students, or applicants on the basis of age, color, disability, sex (including pregnancy), gender, gender identity, gender expression, genetic information, ethnicity or national origin, political affiliation, race, religion, sexual orientation, or military status, or otherwise discriminate against employees or applicants who inquire about, discuss, or disclose their compensation or the compensation of other employees or applicants, or on any other basis protected by law. If you are an individual with a disability and desire an accommodation, please contact Dr. Elinor Benami at elinor@vt.edu during regular business hours at least 10 business days prior to the event.

• Lead user research initiatives using both quantitative and qualitative methods. • Develop and execute research plans, including screeners, interview guides, usability tests scripts, and participant recruitment. • Design, launch, and analyze surveys—interpreting both closed- and open-ended responses to extract trends and user sentiment. • Conduct and moderate user interviews, usability tests, and other qualitative sessions. • Deliver clear, actionable findings through reports and visual presentations. • Collaborate with Product, Design and other cross-functional teams in the Marketplace area to guide decision-making with user-backed evidence. • Oversee freelancers to enable delegation of operational tasks and increase team focus on higher-impact work.

• Design and train large-scale transformer and hybrid foundation models • Own model architecture choices across text, multimodal, and emerging paradigms • Diagnose and resolve training instabilities at scale • Navigate scaling tradeoffs across data, compute, and architecture • Define the technical direction for next-generation models • Apply strong fundamentals in deep learning and representation learning • Design and modify transformer architectures, including: Attention variants, RoPE, ALiBi, Grouped Query Attention (GQA), Mixture-of-Experts (MoE) • Build models from first principles, not just adapt pre-existing codebases • Own optimizer and scheduler choices, including: AdamW, Lion, Adafactor • Understand and debug optimizer instability, gradient pathologies, divergence at large scale • Validate scaling laws and navigate compute vs. data tradeoffs