2022 positions have been filled. If you were not chosen for one of the slots, please consider applying again for the 2023 program, and also for other internships at NASA through https://intern.nasa.gov.

• Project 1: Microbial Growth Potential in CO2 Removal Systems
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  Mentor: Grace Belancik
  

  Description: Without a vastly improved cabin air CO2 removal system technology, deep space missions will not be plausible. Two new CO2 removal methods, liquid amines and cold surface deposition, are currently in development to potentially replace the state of the art system. Microbial contamination is a risk for any spaceflight air revitalization system. Liquid Amines, although inherently a hostile environment for microbial growth, perform more efficiently in a humid atmosphere. Therefore, the potential for microbial growth in the system remains. Alternatively, the CO2 Deposition system (CDep) currently uses an ionic liquid in a membrane to remove bulk humidity from the air prior to removing CO2. The potential for microbial growth in this membrane system and the liquid itself is unknown. The student will perform a critical review of existing literature researching potential for microbial growth in terrestrial amine systems as well as the ionic liquid membrane systems. The results of the student's work will directly inform the designs of both the Liquid Amine and CDep CO2 removal systems by providing a better understanding of microbial growth risk in the respective systems.

  
   
• Project 2: Models of Microbes in Microgravity (and Radiation)
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  Mentor: Jessica Lee
  

  Description: How does evolution work? How do organisms of different species interact with each other and their environment? Some of the deepest fundamental questions of biology are often most effectively asked in the context of microorganisms... and when those microorganisms are simulated in a computer model, it becomes even easier to explore the mechanisms behind evolution and ecology. This is especially relevant to understanding the responses of microbes to life in space, where experiments are difficult to conduct. In this project, you will use computational modeling to simulate and study how microbes live and evolve under different conditions relevant to space, including microgravity and/or radiation. Possible areas of interest include: co-evolution of symbiotic partners; importance of phenotypic diversity; interaction of biology with the physical enviornment. We may use agent-based modeling, or other methods depending on your background and interests. Those interested in developing curricula for outreach to K-12 or undergraduate classrooms are especially encouraged.

  
   
• Project 3: Machine Learning to Study Spaceflight Effects on Biological Systems
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  Mentor: Lauren Sanders
  

  Description: Space travel poses several inherent risks and hazards to human health. In order to mitigate these risks and identify countermeasures, we need to fully understand spaceflight-induced changes at all levels of mammalian physiology (genetic, molecular, tissue, whole-organism). Machine learning techniques are well suited to leverage heterogeneous data types from spaceflown biological samples to create a multi-dimensional model of the complex organismal response to spaceflight. The objective of this project is to use state-of-the-art machine learning approaches to identify spaceflight signatures in biological data from the NASA GeneLab database. The student will perform the modeling, analyze the data, and draw conclusions about the biological signatures of spaceflight.

  
   
• Project 4: Developing New Autonomous Biosensor Technologies for Space Biology Research
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  Mentor: Sergio Santa Maria
  

  Description: NASA Ames leads the development of CubeSats to perform space biology research. BioSentinel is the first biological deep-space CubeSat and will launch aboard Artemis-1 in 2022 and be deployed into deep space from the Artemis-1 aircraft. Data from Biosentinel will be sent by telemetry to Earth. A similar microgravity control experiment (Biosentinel-ISS) will launch to the International Space Station (ISS) in late-December 2021.Analysis of physical samples from Biosentinel-ISS as well as from ground control experiments will help inform the telemetry data from the deep space Biosentinel experiment. Our objective is to investigate the effects of the space radiation environment by measuring the damage response using budding yeast cells. We are currently exploring ways to preserve our Biosentinel-ISS samples for subsequent studies back on Earth and are also performing ground studies in preparation for third Biosentinel mission to the Moon (LEIA); we will investigate the response to chronic low-dose-rate ionizing radiation using different yeast strains. Students will have the opportunity to work with a multidisciplinary team and analyze data from BioSentinel, LEIA, and other R&D projects, as well as learn more about biosensor and payload development, fluidics, optical detection, and mission operations.

  
   
• Project 5: Neuroimmune Responses to Space Radiation
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  Mentor: Egle Cekanaviciute
  

  Description: Health risks of human deep space exploration include damage to the central nervous system and immune dysfunction. In animal models, simulated spaceflight and especially, simulated deep space radiation has been shown to cause neurodegeneration and neuroinflammation as well as peripheral immune dysregulation. The majority of space radiation studies are performed in animal models, which can be difficult to translate to human applications. Therefore, in the Radiation Biophysics Lab, we use human primary cells and organ-on-a-chip system to study human neuroimmune and neurovascular impairments caused by ionizing radiation and other spaceflight factors. In addition, we are comparing the effects of space radiation on human and mouse immune cells. For this project, the student will perform image and data analysis for simulated space radiation effects on either neurovascular models or immune cells and quantify outcomes relevant to cell morphology and differentiation, blood-brain barrier permeability, oxidative stress and inflammation.

  
   
• Project 6: Data Collection and Analysis for Biosignature Studies
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  Mentor: Diana Gentry
  

  Description: Life detection for planetary exploration depends on the characterization, classification and detection of potential biosignatures. The student will learn about the astrobiological techniques used to collect the data, and have the opportunity to learn about real-world challenges in life detection mission concepts, instrument limitations, and processing and analyzing 'noisy' experimental data across different disciplines. The student will assist in data collection, processing, and analysis for statistical classification of biosignatures, including machine learning techniques. Data types include elemental and isotopic abundance, Raman and UV/VIS/NIR spectra, and some sequencing (metagenomic/abundance) data. The parent project team has several custom statistical analysis modules written in MATLAB/Octave, as well as current work under development in Python (including Jupyter Notebook). The student is encouraged to take initiative in suggesting additional sources and types of data that might be informative.

  
   
• Project 7: Investigating Biological Responses to Space Radiation for Biological Missions Beyond Low Earth Orbit (LEO), Using Yeast
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  Mentor: Lauren Liddell
  

  Description: As we plan crewed missions to the Moon, Mars, and beyond, it?s essential to understand how persistent exposure to space radiation affects biology. Unlike on the International Space Station (ISS), where crew support and sample return are possible, experiments for long-duration missions require autonomous systems without sample return.

  NASA's BioSentinel mission will launch in 2022. NASA?s latest biological CubeSat will send the budding yeast, Sacchyromyces cerevisiae, into deep space beyond low Earth orbit (LEO). Data from BioSentinel will be sent by telemetry back to Earth. While human cells would be ideal, limitations in culture methods, extended prelaunch storage, and long flight durations make it difficult to keep them alive. Unlike other model systems, yeast can survive the constraints of long-duration spaceflight. Despite a billion years of evolution separating yeast from humans, we share homology in hundreds of genes important for basic cell function, including responses to DNA damage. BioSentinel uses the redox dye alamarBlue as an indicator of the impacts of radiation on yeast. alamarBlue can only detect general changes to metabolism and growth and cannot detect specific molecular pathways triggered by space radiation.

  The student will use prior and new experimental data to investigate potential molecular mechanisms and metabolic pathways influencing the alamarBlue assay (i.e. stress response, reactive oxygen species, changes in metabolic uptake) to inform future missions beyond LEO. The data analysis /research will also support the development of methods for future analyses.

  
   
• Project 8: Concept Development for a Self-righting Shipping Container for Test Tubes
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  Mentor: Tara Samuels
  

  Description: Space biology research sometimes requires biological specimens such as stem beads or fruit flies to be transported in test tubes. There have been cases where the project requires test tubes be kept upright during shipment. The objective of this project is to design a self-righting shipping container insert that will keep test tubes upright regardless of the orientation of the external shipping container. The project will involve doing some end-user research with scientists and other staff members who have experienced these challenges to fully understand the problem; researching current solutions on the market and existing patents; brainstorming, modeling , and analyzing solutions; and conducting trade studies on such parameters as weight and structural integrity. The final product may be 3D printed and presented as a feasible solution for future test tube transport.

  
   
• Project 9: Space Systems Biology
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  Mentor: Jonathan Galazka
  

  Description: Multiple, interacting environmental factors impact biological systems exposed to spaceflight. Understanding how this complex set of factors affect the hierarchy of molecular networks within living systems requires approaches that measure the state of these systems in totality. The NASA GeneLab project aims to capitalize on advances in analytical technology to accelerate the pace of discovery from precious spaceflight experiments. GeneLab does this by organizing datasets generated from spaceflight opportunities and by generating an open science data stream from un-used spaceflight materials. In this project, students will join the GeneLab team where they will generate and analyze genome-scale datasets from important spaceflight models such as mice, plants and microbes. A particular focus will be on identifying sources of technical noise in these datasets and developing strategies for reducing them. Students will gain experience with short and long read sequencing, data processing and data science.

  
   

• Application Opens: November 1
• Informational Sessions: November 19 and December 10
• Application Deadline: January 10, 5:00 PM PT
• Application Review: Mid-January to Mid-February
• Interview/Offers: Mid-Late February
• Virtual Internship: June 6 to August 12, 2022

• be a US citizen*
• be in high academic standing (GPA of 3.2 or greater)
• have a minimum age of 18
• be a junior or senior undergraduate student next Fall
  -- or --
  a senior graduating in 2022 and entering graduate school next Fall.
• have a passion for space and a desire to study space life science.

Under-represented groups are encouraged to apply.

*Citizens of US territories Puerto Rico, Guam, the U.S. Virgin Islands, and Northern Marianas are U.S. citizens.

Permanent Resident foreign nationals, Legal Resident Aliens, Work Permit holders, and undocumented immigrants are not eligible.

If you are looking for additional/other opportunities with NASA, please visit http://intern.nasa.gov.

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