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Battelle/PNNL

Industry: STEM

Category: Science, Technology, Engineering and Mathematics

Type: Internship

Location: Virtual

Application Deadline: Jun. 20, 2022

Starts: Jun. 20, 2022

Available Positions: 1

$ Paid
 Full Time
 Part Time

Description

Pacific Northwest National Laboratory (PNNL)  is a world-class research institution powered by a highly educated,  diverse workforce committed to the values of Integrity,  Creativity,  Collaboration,  Impact,  and Courage.  Every year,  scores of dynamic,  driven people come to PNNL to work with renowned researchers on meaningful science,  innovations and outcomes for the U.S.  Department of Energy and other sponsors;  here is your chance to be one of them!
 

At PNNL,  you will find an exciting research environment and excellent benefits including health insurance,  flexible work schedules and telework options.  PNNL is located in eastern Washington State—the dry side of Washington known for its stellar outdoor recreation and affordable cost of living.  The Lab’s campus is only a 45-minute flight (or ~3 hour drive)  from Seattle or Portland,  and is serviced by the convenient PSC airport,  connected to 8 major hubs.

 

At a time when complex environmental problems are emerging on every front,  the Nuclear Sciences Division’s staff,  capabilities,  and facilities are delivering science and technology innovations for the environment,  as well as for energy and national security needs.  Our diverse work addresses a wide range of national and international challenges,  from providing solutions that protect the health of people who must work in hazardous environments,  to developing durable new materials,  streamlining industrial processes for improved productivity and effectiveness,  and delivering new approaches for environmental cleanup.  Nuclear Sciences Division capabilities and efforts are focused in three key areas including Environmental Health and Remediation,  Nuclear Regulatory,  and Nuclear Energy.

Responsibilities

Pacific Northwest National Laboratory (PNNL)  is seeking ambitious,  high caliber sophomore,  junior or senior high school students for intern assignments within the Student Research Apprenticeship Program (SRAP).  This summer internship is a research-based experience for students who are sophomore,  juniors or seniors and/or students traditionally underrepresented in science and engineering.

 

The student will leverage the conceptual design tools within the Institute for the Design of Advanced Energy Systems (IDAES)  Process Systems Engineering (PSE)  framework to develop optimized chemical and energysystem designs.  The student will demonstrate the automatic conceptual design toolset with several chemical synthesis systems and apply multiple machine learning (ML)  tools to automate the conceptual design process andenhance the reliability of solutions.  The student will test the reinforcement learning code to automatically generate chemical and energy system design flowsheets,  test the graphic user interface for the reinforcement learning tools and prrepare and help on the documentation for the software

 

****BEFORE YOU APPLY****

When asked to upload your resume to your application,  you need to upload one (1)  PDF file that includes the following:

 

1.  Cover Letter

2.  Resume

3.  Unofficial transcript

4.  Personal Statement:  Describe who you are beyond your academic achievements and potential.  Offer information in regards to what kind of activities,  leadership or experiences you’ve had that contributed toward your interest in STEM.

 

APPLICATIONS THAT DO NOT INCLUDE THE ITEMS ABOVE IN THEIR SINGLE PDF UPLOAD WILL BE DISQUALIFIED AS INCOMPLETE.

Qualifications

Minimum Qualifications:

  • Applicants must be currently enrolled as a sophomore,  junior or senior in a public or private high school at time of application.
  • Applicants must be 16 years of age by start date.
  • Applicants must have a cumulative GPA of 3.0 or higher (from beginning of 9th grade to current standing).

Preferred Qualifications:

  • Has some experiences in Python
  • Has some experiences in one of the software/library:  Jupyter Notebook,  TensorFlow,  PyTorch 
  • Basic knowledge about reinforcement learning