Vacancy Name: IRC75107
Job Title Deep Learning for Computational Imaging Postdoc
Location Los Alamos, NM, US
Organization Name EES-17/Geophysics
What You Will Do
Become part of a team that thrives on challenge, innovation, and mission-driven science. The Geophysics Group ( https://www.lanl.gov/org/ddste/aldcels/earth-environmental-sciences/geophysics/index.php ) in the Earth and Environmental Sciences ( https://ees.lanl.gov ) Division at Los Alamos National Laboratory ( https://www.lanl.gov/ )
We have an immediate opening for a creative and resourceful postdoctoral researcher with strong computational skills and experience in imaging inverse problems and deep learning methods. We are seeking a highly-motivated individual to join a multidisciplinary research team consisting of machine learning scientists, computational scientists and domain experts to conduct cutting-edge machine learning research for computational imaging, with application to subsurface, material and other scientific domains.
The Geophysics Group focuses on a broad array of energy, environmental and national security challenges with research into seismology, infrasound, ground-shock modeling, and acoustics to address critical needs in energy, seismic hazards, threat detection and reduction, earthquake prediction, and subsurface imaging.
What You Need
Minimum Job Requirements:
Strong computational science and numerical optimization skills in particular, computational imaging and inverse problems
Strong deep learning skills and practical experience in various neural network architectures (DNN, CNN, RNN/LSTM, GAN or other auto encoder)
Practical experience with machine learning packages such as PyTorch, TensorFlow, Keras, etc.
Code development and computational experience in using high-performance parallel computing resources
Solid publication record in high-impact journals, top-tier machine learning and related conferences
Excellent communication, writing and oral presentation skills, and
Strong programming skills, in Python in particular.
Demonstrated ability to work creatively and independently and in a team environment.
Ability to obtain DOE Q clearance.
Education : A Ph.D. in Computer Sciences, Applied Math, Computational Sciences, Electrical Engineering or closely related field is required. The candidate must have completed all Ph.D. requirements by commencement of the appointment and be within 5 years of completion of the Ph.D.
Notes to Applicants:
In addition to submitting a CV, please submit a cover letter briefly addressing how you meet the job requirements and the desired skills. CV should include educational background with degree dates and GPAs with scale, experience and expertise, a list of peer-reviewed publications and conference presentations, competitive honors/awards, and contact information of four references.
For further information, check out http://www.lanl.gov/careers/career-options/postdoctoral-research/postdoc-program/index.php . LANL offers an excellent working environment and competitive compensation and benefits package. Additional information about this position can be obtained by contacting Dr. Youzuo Lin ( email@example.com ) and Dr. Brendt Wohlberg ( firstname.lastname@example.org ). For general information about the application processes refer to the LANL Postdoctoral Program page.
No Clearance: Position does not require a security clearance. Selected candidates will be subject to drug testing and other pre-employment background checks.
New-Employment Drug Test: The Laboratory requires successful applicants to complete a new-employment drug test and maintains a substance abuse policy that includes random drug testing.
Candidates may be considered for a Director's Postdoc Fellowship and outstanding candidates may be considered for the prestigious Richard P. Feynman, Darleane Christian Hoffman, J. Robert Oppenheimer, or Frederick Reines Distinguished Postdoc Fellowships.
For general information go to Postdoc Program .
Los Alamos National Laboratory is an equal opportunity employer and supports a diverse and inclusive workforce. All employment practices are based on qualification and merit, without regards to race, color, national origin, ancestry, religion, age, sex, gender identity, sexual orientation or preference, marital status or spousal affiliation, physical or mental disability, medical conditions, pregnancy, status as a protected veteran, genetic information, or citizenship within the limits imposed by federal laws and regulations. The Laboratory is also committed to making our workplace accessible to individuals with disabilities and will provide reasonable accommodations, upon request, for individuals to participate in the application and hiring process. To request such an accommodation, please send an email to email@example.com or call 1-505-665-4444 option 1.
Where You Will Work
Located in northern New Mexico, Los Alamos National Laboratory (LANL) is a multidisciplinary research institution engaged in strategic science on behalf of national security. LANL enhances national security by ensuring the safety and reliability of the U.S. nuclear stockpile, developing technologies to reduce threats from weapons of mass destruction, and solving problems related to energy, environment, infrastructure, health, and global security concerns.
The Earth and Environmental Sciences Division is a multi‐program research organization with core capabilities in Geology, Geochemistry, Geophysics, Geomaterials, Geography, Hydrology, Petroleum Engineering, Chemical Engineering, Acoustics, Atmospheric Science, Ecology, Environmental Science, Computational Science, and Geotechnical Engineering. Cross‐cutting capabilities and facilities span the needs for laboratory experimentation, ﬁeld deployments, computing, and data analytics. Capabilities in the Division are organized into groups, which consist of scientists, postdoctoral researchers, technicians, administrators, and students. Most programs within the Division feature multi‐discipline teams organized to solve complex problems of national importance.
Appointment Type Postdoc
Contact Name Lin, Youzuo
Req ID: IRC75107