Job Opportunities

NIST Professional Research Experience Program (PREP)

BACKGROUND INFORMATION

The National Institute of Standards and Technology’s (NIST’s) Material Measurement Laboratory (MML) serves as the national reference laboratory for measurements in the chemical, biological and material sciences through activities ranging from fundamental and applied research, to the development and dissemination of certified reference materials, critically evaluated data, and other programs and tools to assure the quality of measurement results. One of the program areas where MML has significant research efforts in the field of forensic science, specifically drug chemistry.  A major project within this research area is the development of methods and tools for next generation chemical analyses to provide to the community.  A key technology for next generation analysis is direct analysis in real time mass spectrometry (DART-MS) which has been shown to be to detect a number of forensically relevant compounds including drugs of abuse, explosives, chemical warfare agents, lotions and lubricants, and toxic industrial chemicals (TICs) in seconds.  While the technology shows promise, especially for forensic analysis of seized drugs, the data analysis process is currently cumbersome and lacks key features needed by the community. 

To help address the data analysis limitations, work in MML has been ongoing on the development of an update DART-MS spectral database (DBB) and new DART-MS search algorithms (DST).  With the database and search algorithm, forensic chemists will be able to analyze their data more accurately, more efficiently, and with increased confidence over traditional programs.  The intent is to bring the database and search algorithm from a research tool to something that is implementable by the forensics community through the development of a user-friendly interface and additional features or functions. These features will include report generation functionalities, commenting capabilities, and additional functionalities that will be identified through iterative feedback with four collaborators within the forensics community.  Iterative updates to the software will be expected throughout the process with a finished version of the software completed by the end of the award. It is also expected that technical documentation will be developed to accompany the software.

SCOPE

In collaboration with NIST staff, the awardee will be working to improve the newly implemented NIST DART-MS Search Tool (NIST DST). In particular, the focus is on developing auxiliary functionality that improves the overall usability of the NIST DST, while NIST Scientists continue to optimize the underlying search functionality. Additionally, the documentation must be drafted to aid the use and continued development of the DST. The NIST DST is an R Shiny application and is intended for use across multiple operating systems. The current version of the NIST DST can be found here: https://github.com/asm3-nist.

GOALS

  1. Improve user interface with ‘Reporting’ functionality.

A preliminary version of the NIST DST has been developed as an R Shiny Application. Using this codebase as a starting point, the awardee will improve the usability of the NIST DST by refining the user interface and developing a ‘Reporting’ function to the specifications recommended by NIST staff and collaborators. Software and supporting document updates will be periodically shared with NIST collaborators.

  1. Incorporate user feedback.  

Feedback about software and supporting document updates will be solicited and collated by NIST staff and provided to the awardee as updated specifications. The awardee will refine the NIST DST accordingly.

  1. Support implementation of additional functions.

NIST staff will be optimizing the search functionality of the DST. The awardee will support the implementation of the updated functions into the main DST codebase.

Development Process

NIST staff will be making code changes at the same time as the participant.  Git is used for version control (NIST uses an internal GitLab instance as the source repo).  Both the participant and NIST staff shall commit code changes under their respective accounts. 

Software will be developed continuously through the duration of the award with regular meetings to discuss progress. Additionally, there will be three major checkpoints where a clone of the software will be shared with collaborators to solicit feedback.

PREP Candidate

NIST is looking for a Software Developer with a bachelor’s degree or equivalent; minimum of 2 years’ experience in the R programming language; use of the R Shiny framework; and experience in software development and design. It will be useful if this person has experience working with chemical data and experience working in a research and development environment.

Position: The position can be full-time or part-time, depending on the circumstances and skills of the successful  candidate.

Salary:  Salary will be commensurate with experience.

Candidates should send their CV to  edward.sisco@nist.gov or arun.moorthy@nist.gov

The Applied Economics Office (AEO) of the Engineering Laboratory at the National Institute of Standards of Technology (NIST) anticipates the need for a Post-Bachelors, Graduate Student, Master’s Degree Holder, or Postdoc in computer science or a related field to develop web applications that enable complex economic analysis.

The position would be supported through the NIST Professional Research Experience Program (PREP) (https://www.nist.gov/iaao/academic-affairs-office/nist-professional-research-experience-program-prep).

Proposed start date: negotiable
Proposed duration: 1 year with potential for extension
Position Location: Telework

General Duties and Responsibilities

Responsibilities include, but are not limited to:

  • Develop user interface for web applications
  • Develop server-side code including algorithms related to economic analysis
  • Assist with special software development projects as assigned

Requirements

  • Degree in Computer science, Software engineering, Web development, Programming or related field.
  • HTML, JavaScript, CSS, JSON, Flask or another web framework
  • Python, Ruby, open source databases a plus
  • Google Analytics and data visualization experience a plus
  • Logical thinking and Problem solving
  • Patience and Attention to detail
  • Excellent writing, code documentation and testing skills

About AEO

The AEO (Applied Economics Office), a part of the Engineering Laboratory (EL) at NIST, provides economic products and services through research and consulting to industry and government agencies in support of productivity enhancement, economic growth, and international competitiveness, with a focus on improving the life-cycle quality and economy of constructed facilities and manufacturing processes that support social and economic functions.

The AEO is integrated within EL’s major research thrusts: sustainability, energy conservation, community resilience planning, manufacturing, fire, smart grid, building construction, and safety. The AEO delivers high quality research and tool development that informs and assists stakeholders in their decision-making processes.

Please submit a single .pdf file with a full curriculum vitae, and names and contact information of two professional references with email Subject Line “AEO PREP Software Developer” to:

Professor Peter Olmsted
Department of Physics
peter.olmsted@georgetown.edu

Applications will be forwarded to NIST for consideration.

Qualified applicants are invited to apply for postdoctoral research opportunities at the US National Institute of Standards and Technology in Gaithersburg, Maryland.  A major aim of our group is to use artificial intelligence to improve mass spectrometry in the fields of proteomics and metabolomics.  Our current research objective is to design and train deep neural networks to predict the mass spectra of all peptides in the human genome and to estimate the error of these predictions.  These predicted spectra may be incorporated into the NIST mass spectral libraries, used by many thousands of scientists worldwide to analyze complex biological and chemical samples.

This opportunity is ideal for a scientist with a strong background in computation, math, and physical or biological sciences to learn artificial intelligence methods.  A background in machine learning and mass spectrometry is not required, but the successful applicant should program in python and understand both linear algebra and statistics.  Coursework or research in biology and chemistry is a plus.  The applicant will work closely with and be mentored by scientists well versed in machine learning and mass spectrometry. US citizenship is not required.

NIST is a center for measurement excellence, and, as a result, has available for this research large sets of high quality data essential for deep learning.  NIST also provides training and excellent computational support, including substantial GPU compute clusters.

The successful applicant would have an opportunity to interact with the large community of chemists, biologists, physicists and computer scientists at NIST, and be encouraged to present their research in a variety of international meetings and publications. 

Interested candidates are invited to send a CV to Dr Lewis Geer, lewis.geer@nist.gov (new window)

The Applied Economics Office (AEO) of the Engineering Laboratory at the National Institute of Standards of Technology (NIST) anticipates the need for an Undergraduate Student in computer science or a related field to develop web applications that enable complex economic analysis.

The position would be supported through the NIST Professional Research Experience Program (PREP) (https://www.nist.gov/iaao/academic-affairs-office/nist-professional-research-experience-program-prep).

Proposed start date: negotiable
Proposed duration: 1 year with potential for extension
Position Location: Telework

General Duties and Responsibilities

Responsibilities include, but are not limited to:

  • Develop user interface for web applications
  • Develop server-side code including algorithms related to economic analysis
  • Assist with special software development projects as assigned

Requirements

  • Degree in Computer science, Software engineering, Web development, Programming or related field.
  • HTML, JavaScript, CSS, JSON, Flask or another web framework
  • Python, Ruby, open source databases a plus
  • Google Analytics and data visualization experience a plus
  • Logical thinking and Problem solving
  • Patience and Attention to detail
  • Excellent writing, code documentation and testing skills

About AEO

The AEO (Applied Economics Office), a part of the Engineering Laboratory (EL) at NIST, provides economic products and services through research and consulting to industry and government agencies in support of productivity enhancement, economic growth, and international competitiveness, with a focus on improving the life-cycle quality and economy of constructed facilities and manufacturing processes that support social and economic functions.

The AEO is integrated within EL’s major research thrusts: sustainability, energy conservation, community resilience planning, manufacturing, fire, smart grid, building construction, and safety. The AEO delivers high quality research and tool development that informs and assists stakeholders in their decision-making processes.

Please submit a single .pdf file with a full curriculum vitae, and names and contact information of two professional references with email Subject Line “AEO PREP Software Developer” to:

Professor Peter Olmsted
Department of Physics
peter.olmsted@georgetown.edu

Applications will be forwarded to NIST for consideration.

Title:  Word Embeddings of Maintenance Work Order Data
Location:   NIST, Gaithersburg MD
Salary: $15-$30 per hour, 10-40 hours/week, in the period May 1 2020 to 30 April 2021

The Knowledge Extraction and Application for Manufacturing Operations project (https://www.nist.gov/programs-projects/knowledge-extraction-and-application-manufacturing-operations (new window)) is planning to create GloVe (Global Vectors for Word Representation) and BERT (Bidirectional Encoder Representations from Transformers)-style embeddings specialized for the analysis of technical datasets, containing short sentences, informal language, abbreviations, and technical jargon (e.g., maintenance logs). This effort will begin with the curation of maintenance-related text in the form of technical specifications, maintenance manuals, and maintenance logs. It will then continue with the creation of embeddings from the collected text and their evaluation for use in specific maintenance-related analyses. The goal of this work is to learn differences and similarities between NLP techniques for “normal”, non-technical documents and technical, informal documents. The desired student will have the following attributes:  

  • Working on or completed BS or MS in Computer Science, Information Systems, Computational Logistics, or related field with a focus on text analysis, natural language processing, data science, and machine learning.
  • Demonstrated experience with Python and familiarity with its NLP, data analysis, and machine learning packages.
  • Comfortable working with command-line Linux on large scientific server platforms.
  • Willingness to work across the full data science lifecycle from data acquisition and curation through analysis and publication.

Application process: Send a short CV, including courses taken and relevant experience, to peter.olmsted@georgetown.edu (new window), and fill out an expression of interest form (new window) at https://softmatter.georgetown.edu/nist-professional-research-experience-program-prep/ (new window)

The Georgetown/NIST Professional Research Experience Program seeks a post-doc to work on the following topic:

High-Sensitivity Infrared Microscopy for Live Cells
Currently, no imaging method is capable of concentration mapping with chemical identification despite the extensive practice of optical characterization of cells. The new NIST-developed infrared (IR)  imaging strategy will lead to label-free, in-situ cytometry of critical cell characteristics from viability to identity, and the results will be system-independent and universally reproducible. Based on recent breakthroughs in IR spectral engineering, our proposed IR absorbance imaging will eliminate the strong water absorption contribution and see the molecular signatures of biological samples in water or in their natural environment. The new IR spectral engineering technique has been demonstrated for high sensitivity characterization of protein structures. We also propose to apply this advanced spectroscopy method to noninvasive monitoring of protein drugs production.

Benefits to NIST

The SI-traceable IR imaging concept is a revolutionary advancement for the in-situ quantitative chemical imaging of all cell biology and cell therapeutics and has the potential to advance engineering biology, biomanufacturing, cancer research, and forensics.Success of this high-risk but high-impact project, leveraged by NIST’s unique strengths in hyperspectral imaging, optical cell measurements, microfluidics, and machine learning, will advance the quantitative cell metrology and boost measurement assurance directly in cell therapeutics and regenerative medicine and more broadly biomanufacturing, engineering biology, and pharma industries.

The Biomaterials Group has been developing new vibrational spectroscopic technologies aimed for characterizing protein drugs. This is directly related to the NIST strategic program of Biomanufacturing Initiatives. Success in this project requires expertise in both vibrational spectroscopy and bioimaging, which are not available easily. This work enables NIST to address the needs of stakeholders (biomanufacturing and biomedical industries and FDA) for advanced analytical tools with higher sensitivity and specificity. In addition, this work outcome can be applied to various process analytical technology (PAT) approaches during the development and production of protein drugs, vaccines, adjuvant-drug-conjugate drugs, and other medicinal products.

Position Details

We seeking a PhD-level automation scientist or engineer.  The person hired for this position will be stationed at the NIST Gaithersburg Campus in Gaithersburg, Maryland, close to Washington DC area. The person hired for this position will help to lead the development of imaging-based spectroscopic methods to characterize biomolecular signatures in live cells and complex biomolecules. The person will work with a multidisciplinary team at NIST to develop new bioimaging methods based on Raman and infrared signals.

Required Qualifications:

  • PhD. in chemistry, physics, biology, engineering, or related field
  • Experience working with Raman and infrared spectroscopy systems
  • Experience operating and developing methods for imaging biological systems
  • Ability to troubleshoot and resolve issues that arise with laser and spectroscopy equipment and other general laboratory equipment
  • Experience with programming languages used for scientific data analysis, including, for example, LabView, MatLab
  • Basic working knowledge of laser spectroscopy or microscopy and demonstrated problem-solving skills, and the ability to identify, manage, and overcome technical hurdles
  • Good communications, laboratory, and organizational skills; the ability to work independently and with a multi-disciplinary team

Desired Qualifications:

  • Hands-on experience with spectroscpic analysis of complex molecules and working with optical systems
  • Experience with LabView programming for controlling multiple instruments
  • Experience with vibrational spectroscopy and imaging instrumentation, for example, broadband CARS microscopy and QCL-infrared microscopy
  • Hands-on experience handling hyperspectral image data
  • Experience working in or with laser spectroscopy and analytical chemistry laboratories
  • Experience in cell imaging and protein sample handling
  • Experience managing and coordinating projects
  • US citizenship preferred

Salary: $68,000/year for two years with $2500 travel funds.

Please submit a single .pdf file with a full curriculum vitae, and names and contact information of two professional references to:

Professor Peter Olmsted
Department of Physics
peter.olmsted@georgetown.edu

Applications will be forwarded to NIST for consideration.

Engineering Laboratory (EL), Materials and Structural Systems Division, Infrastructure Materials Group at the National Institute of Standards and Technology (NIST), Gaithersburg, MD

Principal Objectives

The Infrastructure Materials Group of the National Institute of Standards and Technology (NIST) has an opening for an undergraduate researcher through the PREP program for spring semester 2021. The selected candidate will work with a team to collect and qualitatively analyze hydraulic cement expansion and microstructure evolution in mortars exposed to sulfate ions. Sulfate-related reactions and expansion poses challenges for durable concrete under certain environmental exposure conditions. The resulting data will be evaluated relative to cement phase composition, chemistry, and particle size distribution to identify key predictors of susceptible cements. These data will be useful in defining new compositional limits for cements and for developing statistical models to predict a cement’s resistance to sulfate exposure. Following the completion of analysis, the student may contribute to a publication describing the methods used and main findings of this research.

General Information

This project is supporting research being conducted by the NIST Engineering Materials for Resilient Infrastructure Program (EMRI). EMRI objectives are to develop and promote measurement science to reliably assess the current and future performance of engineered materials in support of resilient civil infrastructure given exposure to chronic (e.g., materials degradation) and episodic (e.g., earthquakes) hazards.

Qualifications

Required

  • Pursuing an undergraduate degree in topics related to or an interest in civil engineering, chemistry, geology, and materials science.
  • Laboratory experience.
  • Experience with MS Excel, MS Word, and an interest in learning qualitative and quantitative analysis methods.
  • Available for research, 20 hours per week in Spring Semester 2021

Preferred

  • Physical testing of cement and concrete materials
  • Experience interpreting and applying ASTM standard test methods
  • Experience in data analysis using Excel and R or Python

Please Send CV and at least two Professional References to:

Professor Peter Olmsted
Peter.Olmsted@georgetown.edu