Los Alamos National Laboratory Machine Learning for Plant-Microbiome Interactions Postdoc in Los Alamos, New Mexico
What You Will Do
The Computer, Computational, and Statistical Sciences Division at Los Alamos National Laboratory (LANL) is seeking outstanding candidates for a postdoctoral research associate position in bioinformatics and machine learning for plant-microbiome interactions. The position is available immediately. The postdoc will work in conjunction with a team of Ph.D. staff scientists, postdoctoral researchers, and technicians as part of a multi-disciplinary research project focused on microbiome-plant interactions. The project combines metagenomics, plant physiology, soil and tissue chemistry, epigenetics, and machine learning techniques to investigate microbiome-control of plant drought tolerance, with maize as a test system. The successful candidate should have a strong publication record, open mind to explore boundaries of scientific fields, and ability to effectively communicate with researchers with varied backgrounds. The postdoctoral research associate is expected to publish results of the work in high-impact peer-reviewed journals and present at well-known conferences. The position is for two years, with a third year possible contingent on excellent performance.
What You Need
Minimum Job Requirements:
Ph.D. in a STEM field received within the last 5 years
Strong track record of peer-reviewed publications within bioinformatics, Bayesian analysis, or machine learning
Excellent presentation, oral and written communication skills
Experience with computer programming for data analysis (with e.g. Python, R, Julia, …)
Expertise in bioinformatics and biological networks, especially in microbiome analysis.
Experience with Probabilistic Graphical Modeling and Bayesian analysis
Expertise in data science, machine learning, and statistics
Demonstrated experience in interdisciplinary science, especially in the many areas mentioned above
Ph.D. in bioinformatics, statistics, applied mathematics, machine learning, or a related discipline completed within the last 5 years. Applications from candidates who will earn their degree by the time of appointment are accepted.
Note to Applicants:
The application package consists of a CV and a cover letter. In addition to applying online, applicants may email their CV and cover letter to Nicholas Lubbers ( firstname.lastname@example.org , 505-665-3916) and Sanna Sevanto ( email@example.com , 505-664-0232). No applicant is expected to have all of the desired skills. Anyone who meets the education and minimum job requirements is encouraged to apply.
Exceptional candidates may be considered for a Center for Non-Linear Studies Postdoc (for more information, see http://cnls.lanl.gov ) and/or Director's Fellowship. Outstanding candidates may be considered for the prestigious Marie Curie, Richard P. Feynman, J. Robert Oppenheimer, or Frederick Reines Fellowships.
For general information about the Postdoc Program, including salary guidelines, go to http://www.lanl.gov/careers/career-options/postdoctoral-research/postdoc-program/index.php.
Interested candidates should apply online to job number IRC79493 at https://www.lanl.gov/careers/career-options/index.php
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.
Internal Applicants: Regular appointment employees who have served at least one year of continuous service in their current position are eligible to apply for posted jobs throughout the Laboratory. If an employee has not served the one year of continuous service, they may only apply for Laboratory jobs with the documented approval of their Division Leader.Please refer to Laboratory Policy P701 for applicant eligibility requirements.
Equal Opportunity: 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 regard 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 firstname.lastname@example.org 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 Information Sciences Group (CCS-3) engages in a wide variety of basic and applied research activities in areas such as machine learning, sensors, knowledge information systems, and quantum computing. CCS-3 provides the critical expertise necessary to address tomorrow’s data-intensive National Security challenges. From the development of novel deep-learning neural architectures for data-driven decision making, to the invention of new algorithms that take advantage of revolutionary hardware such as quantum and neuromorphic circuitry, we seek to change the world by pushing the boundary of what is possible in the here and now.
Contact Name Doyle, Christine Louise
Vacancy Name: IRC79493
Organization Name CCS-3/Information Sciences
Req ID: IRC79493