ORAU
Overview
Foundations of Learning Systems Research
— join to apply for this role at ORAU.
Responsibilities
Mathematical development of new machine learning algorithms, with emphasis on kernel and Bayesian methods
Co-develop deep signal representations such as auto-encoders or wavelet scattering transforms
Integrate these methods with adaptive control (iterative learning control), reinforcement learning, and hierarchical, scenario-based decision systems such as random forests
Organization DEVCOM Army Research Laboratory (ARL) — Computational and Information Sciences Directorate (CISD)
Reference Code:
ARL-C-CISD-300077
Description Machine learning is expected to play a key role in future Army autonomous systems, both virtual and robotic. Opportunities exist at ARL to advance the application and development of Artificial Intelligence and Machine Learning for autonomy. This opportunity emphasizes mathematical development of new machine learning algorithms, with a particular emphasis on kernel and Bayesian methods, and their co-development with deep signal representations such as auto-encoders or wavelet scattering transforms. These will be coupled with adaptive control (iterative learning control), reinforcement learning, and hierarchical scenario-based decision systems such as random forests.
About CISD The Computational and Information Sciences Directorate (CISD) conducts research in disciplines relevant to achieving and implementing the so-called digital battlefield. Problems address the sensing, distribution, analysis, and display of information in the modern battle space. CISD research focuses on four major areas: communications, atmospheric modeling, battlefield visualization, and computing.
About ARL-RAP The Army Research Laboratory Research Associateship Program (ARL-RAP) is designed to significantly increase the involvement of creative and highly trained scientists and engineers from academia and industry in scientific and technical areas of interest and relevance to the Army. Scientists and Engineers at ARL help shape and execute the Army's program for meeting the challenge of developing technologies that will support Army forces in meeting future operational needs by pursuing scientific research and technological developments in diverse fields such as: applied mathematics, atmospheric characterization, simulation and human modeling, digital/optical signal processing, nanotechnology, material science and technology, multifunctional technology, propulsion and flight physics, communication and networking, and computational and information sciences.
A complete application includes
Curriculum Vitae or Resume
Three References Forms
Transcripts
Notes: an email with a link to the reference form will be available to the applicant upon completion of the online application. References should be from persons familiar with your educational and professional qualifications (include your thesis or dissertation advisor, if applicable). Transcripts verifying degree must be submitted; student/unofficial copies are acceptable.
Research proposal (ARL-RAP) If selected by an advisor, the participant will be required to write a
research proposal
to submit to the ARL-RAP review panel. The proposal should relate to a specific ARL opportunity (see Research Areas), have a defined objective and outcome, explain the direction you plan to pursue, include the expected period for completing the study, and include a brief background such as preparation and motivation for the research; references of published efforts may be used to improve the proposal.
Questions Please email
ARLFellowship@orau.org
with any questions about this opportunity.
Eligibility Requirements
Degree: Master’s Degree or Doctoral Degree
Academic Level: Faculty
Discipline(s): Computer, Information, and Data Sciences; Artificial Intelligence (including Robotics, Computer Vision, and Human Language Processing); Computer Architecture and Grids; Computer Science – Languages and Systems; Computer Science – Theoretical Foundations; Computer Science (general); Software Engineering
Seniority level Internship
Employment type Full-time
#J-18808-Ljbffr
— join to apply for this role at ORAU.
Responsibilities
Mathematical development of new machine learning algorithms, with emphasis on kernel and Bayesian methods
Co-develop deep signal representations such as auto-encoders or wavelet scattering transforms
Integrate these methods with adaptive control (iterative learning control), reinforcement learning, and hierarchical, scenario-based decision systems such as random forests
Organization DEVCOM Army Research Laboratory (ARL) — Computational and Information Sciences Directorate (CISD)
Reference Code:
ARL-C-CISD-300077
Description Machine learning is expected to play a key role in future Army autonomous systems, both virtual and robotic. Opportunities exist at ARL to advance the application and development of Artificial Intelligence and Machine Learning for autonomy. This opportunity emphasizes mathematical development of new machine learning algorithms, with a particular emphasis on kernel and Bayesian methods, and their co-development with deep signal representations such as auto-encoders or wavelet scattering transforms. These will be coupled with adaptive control (iterative learning control), reinforcement learning, and hierarchical scenario-based decision systems such as random forests.
About CISD The Computational and Information Sciences Directorate (CISD) conducts research in disciplines relevant to achieving and implementing the so-called digital battlefield. Problems address the sensing, distribution, analysis, and display of information in the modern battle space. CISD research focuses on four major areas: communications, atmospheric modeling, battlefield visualization, and computing.
About ARL-RAP The Army Research Laboratory Research Associateship Program (ARL-RAP) is designed to significantly increase the involvement of creative and highly trained scientists and engineers from academia and industry in scientific and technical areas of interest and relevance to the Army. Scientists and Engineers at ARL help shape and execute the Army's program for meeting the challenge of developing technologies that will support Army forces in meeting future operational needs by pursuing scientific research and technological developments in diverse fields such as: applied mathematics, atmospheric characterization, simulation and human modeling, digital/optical signal processing, nanotechnology, material science and technology, multifunctional technology, propulsion and flight physics, communication and networking, and computational and information sciences.
A complete application includes
Curriculum Vitae or Resume
Three References Forms
Transcripts
Notes: an email with a link to the reference form will be available to the applicant upon completion of the online application. References should be from persons familiar with your educational and professional qualifications (include your thesis or dissertation advisor, if applicable). Transcripts verifying degree must be submitted; student/unofficial copies are acceptable.
Research proposal (ARL-RAP) If selected by an advisor, the participant will be required to write a
research proposal
to submit to the ARL-RAP review panel. The proposal should relate to a specific ARL opportunity (see Research Areas), have a defined objective and outcome, explain the direction you plan to pursue, include the expected period for completing the study, and include a brief background such as preparation and motivation for the research; references of published efforts may be used to improve the proposal.
Questions Please email
ARLFellowship@orau.org
with any questions about this opportunity.
Eligibility Requirements
Degree: Master’s Degree or Doctoral Degree
Academic Level: Faculty
Discipline(s): Computer, Information, and Data Sciences; Artificial Intelligence (including Robotics, Computer Vision, and Human Language Processing); Computer Architecture and Grids; Computer Science – Languages and Systems; Computer Science – Theoretical Foundations; Computer Science (general); Software Engineering
Seniority level Internship
Employment type Full-time
#J-18808-Ljbffr