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DTSTART;TZID=America/Denver:20231115T100000
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UID:submissions.supercomputing.org_SC23_sess503_job198@linklings.com
SUMMARY:Research Scientist - AI For Scientific Data Management
DESCRIPTION:Overview: \n\nInterested in crafting the future of scientific 
 data management and metadata organization through the power of AI? Your ex
 pertise and motivation will drive impactful innovations that transform the
  way we approach and understand robust scientific datasets. You will have 
 the chance to work in a supportive and innovative environment, supplying r
 esearch that has the potential to impact scientific understanding! \n\n \n
 \nWe are seeking a dynamic and experienced research professional in Artifi
 cial Intelligence (AI) for Scientific Data Management to join this team. T
 his is an exciting opportunity to participate in innovative research and d
 evelopment at the intersection of AI, scientific data management, and meta
 data organization. You will play a pivotal role in advancing our research 
 initiatives by harnessing artificial intelligence to manage, analyze, and 
 derive insights from large-scale scientific datasets while also focusing o
 n effective metadata management. You will advance scientific knowledge and
  discovery by developing pioneering technologies that will aid in data col
 lection, curation, and address sensitive data policies with openly sharing
  federally funded research datasets. You will collaborate closely with mul
 tidisciplinary teams of scientists, engineers, and data experts to develop
  and implement innovative AI solutions. This position resides in the Data 
 Lifecycle Technologies group in the Advanced Technologies section, Nationa
 l Center for Computational Sciences, Computing and Computational Sciences 
 Directorate, at Oak Ridge National Laboratory (ORNL).  \n\n \n\nAs a U.S. 
 Department of Energy (DOE) Office of Science national laboratory, ORNL has
  an extraordinary 80-year history of solving the nation’s biggest problems
 . We have a dedicated and creative staff of over 6,000 people! Our vision 
 for diversity, equity, inclusion, and accessibility (DEIA) is to cultivate
  an environment and practices that foster diversity in ideas and in the pe
 ople across the organization, as well as to ensure ORNL is recognized as a
  workplace of choice. These elements are critical for enabling the executi
 on of ORNL’s broader mission to accelerate scientific discoveries and thei
 r translation into energy, environment, and security solutions for the nat
 ion. \n\n \n\nMajor Duties/Responsibilities: \n\n<li 335552541="" aria-set
 size="-1" data-aria-level="1" data-aria-posinset="1" data-font="Symbol" da
 ta-leveltext="" data-list-defn-props="{">\nLeverage AI techniques, includ
 ing machine learning, deep learning, and natural language processing, to d
 evelop intelligent data management strategies tailored to scientific domai
 ns. \n\n<li 335552541="" aria-setsize="-1" data-aria-level="1" data-aria-p
 osinset="1" data-font="Symbol" data-leveltext="" data-list-defn-props="{"
 >\nDesign and implement algorithms for data preprocessing, feature extract
 ion, pattern recognition, and predictive modeling to enhance scientific da
 ta analysis. \n\n<li 335552541="" aria-setsize="-1" data-aria-level="1" da
 ta-aria-posinset="2" data-font="Symbol" data-leveltext="" data-list-defn-
 props="{">\nCollaborate with domain leaders to understand the unique chall
 enges of scientific data management and propose novel solutions. \n\n<li 3
 35552541="" aria-setsize="-1" data-aria-level="1" data-aria-posinset="3" d
 ata-font="Symbol" data-leveltext="" data-list-defn-props="{">\nDevise str
 ategies for effective metadata capture, curation, and enrichment to enhanc
 e data discoverability, interoperability, and reusability. \n\n<li 3355525
 41="" aria-setsize="-1" data-aria-level="1" data-aria-posinset="4" data-fo
 nt="Symbol" data-leveltext="" data-list-defn-props="{">\nDevelop automate
 d methods for metadata extraction and tagging, enabling efficient organiza
 tion and search-ability of scientific datasets. \n\n<li 335552541="" aria-
 setsize="-1" data-aria-level="1" data-aria-posinset="5" data-font="Symbol"
  data-leveltext="" data-list-defn-props="{">\nIntegrate metadata manageme
 nt solutions with existing data management systems and tools. \n\n<li 3355
 52541="" aria-setsize="-1" data-aria-level="1" data-aria-posinset="1" data
 -font="Symbol" data-leveltext="" data-list-defn-props="{">\nStay abreast 
 of the latest advancements in AI, scientific data management, and metadata
  standards, integrating relevant technologies and methodologies into resea
 rch projects. \n\n<li 335552541="" aria-setsize="-1" data-aria-level="1" d
 ata-aria-posinset="2" data-font="Symbol" data-leveltext="" data-list-defn
 -props="{">\nPrepare research publications, technical reports, and grant p
 roposals.  \n\n<li 335552541="" aria-setsize="-1" data-aria-level="1" data
 -aria-posinset="3" data-font="Symbol" data-leveltext="" data-list-defn-pr
 ops="{">\nProvide mentorship and guidance to junior researchers and contri
 bute to a dynamic, innovative research environment. \n\n<li 335552541="" a
 ria-setsize="-1" data-aria-level="1" data-aria-posinset="4" data-font="Sym
 bol" data-leveltext="" data-list-defn-props="{">\nDeliver ORNL’s mission 
 by aligning behaviors, priorities, and interactions with our core values o
 f Impact, Integrity, Teamwork, Safety, and Service. Promote diversity, equ
 ity, inclusion, and accessibility by fostering a respectful workplace – in
  how we treat one another, work together, and measure success.  \n\n \n\nB
 asic Qualifications: \n\n<li 335552541="" aria-setsize="-1" data-aria-leve
 l="1" data-aria-posinset="1" data-font="Symbol" data-leveltext="" data-li
 st-defn-props="{">\nPhD in computer science or related field, with a stron
 g background in artificial intelligence, machine learning, data science, o
 r a related subject area.  \n\n<li 335552541="" aria-setsize="-1" data-ari
 a-level="1" data-aria-posinset="2" data-font="Symbol" data-leveltext="" d
 ata-list-defn-props="{">\n2+ years of experience in AI/ML research after d
 egree. \n\n \n\nPreferred Qualifications: \n\n<li 335552541="" aria-setsiz
 e="-1" data-aria-level="1" data-aria-posinset="3" data-font="Symbol" data-
 leveltext="" data-list-defn-props="{">\n5+ years of experience in AI/ML r
 esearch with data management focus. \n\n<li 335552541="" aria-setsize="-1"
  data-aria-level="1" data-aria-posinset="4" data-font="Symbol" data-levelt
 ext="" data-list-defn-props="{">\nConsistent record of mid-career researc
 h accomplishments, demonstrated through publications, patents, or impactfu
 l projects in AI and scientific data management. \n\n<li 335552541="" aria
 -setsize="-1" data-aria-level="1" data-aria-posinset="1" data-font="Symbol
 " data-leveltext="" data-list-defn-props="{">\nProficiency in programming
  languages such as Python or Julia, and experience with AI frameworks (e.g
 ., TensorFlow, PyTorch) and data processing tools (e.g., Spark, Hadoop). \
 n\n<li 335552541="" aria-setsize="-1" data-aria-level="1" data-aria-posins
 et="2" data-font="Symbol" data-leveltext="" data-list-defn-props="{">\nSo
 lid understanding of scientific data types, formats, and challenges, drive
  to tailor AI solutions to specific research domains. \n\n<li 335552541=""
  aria-setsize="-1" data-aria-level="1" data-aria-posinset="3" data-font="S
 ymbol" data-leveltext="" data-list-defn-props="{">\nExperience in metadat
 a standards, with a focus on capturing, organizing, and managing metadata 
 for scientific datasets. \n\n<li 335552541="" aria-setsize="-1" data-aria-
 level="1" data-aria-posinset="4" data-font="Symbol" data-leveltext="" dat
 a-list-defn-props="{">\nStrong analytical and problem-solving skills, tend
 ency to approach sophisticated issues from a multidisciplinary perspective
 . \n\n<li 335552541="" aria-setsize="-1" data-aria-level="1" data-aria-pos
 inset="5" data-font="Symbol" data-leveltext="" data-list-defn-props="{">\
 nExcellent communication skills, both written and verbal, strive to collab
 orate effectively with teams with varied strengths and present research fi
 ndings to both technical and non-technical audiences. \n\n \n\nORNL offers
  competitive pay and benefits programs to attract and retain hardworking p
 eople. The laboratory offers many employee benefits, including medical and
  retirement plans and flexible work hours, to help you and your family liv
 e happy and healthy. Employee amenities such as on-site fitness, banking, 
 and cafeteria facilities are also provided for convenience. \n\nIn additio
 n, we offer a flexible work environment that supports both the organizatio
 n and the employee. A hybrid/onsite working arrangement may be available w
 ith this position. \n\n \n\nOther benefits include: Prescription Drug Plan
 , Dental Plan, Vision Plan, 401(k) Retirement Plan, Contributory Pension P
 lan, Life Insurance, Disability Benefits, Generous Vacation and Holidays, 
 Parental Leave, Legal Insurance with Identity Theft Protection, Employee A
 ssistance Plan, Flexible Spending Accounts, Health Savings Accounts, Welln
 ess Programs, Educational Assistance, Relocation Assistance, and Employee 
 Discounts.\n\n
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