What You Will Do
Come join the best and brightest minds in the world at one of the most innovative and creative multidisciplinary research institutions! The Information Sciences Group (CCS-3) in the Computer, Computational and Statistical Sciences Division at Los Alamos National Laboratory (LANL), in collaboration with the Physics and Chemistry of Materials Group (T-1) in the Theoretical Division, are recruiting a highly motivated post-doctoral research associate. The successful candidate will be part of a multi-divisional team working on development of an integrated and automated multiscale simulation capability, driven by exascale computing, data-driven methods including but not limited to machine learning, and rigorous uncertainty quantification. The successful candidate will be expected to work in an interdisciplinary team environment and interact with scientists working in material science, data science, statistical physics, machine learning, in different organizations of the Laboratory (CCS-3/T-1/CCS-7). The candidate will develop and implement data-driven and dynamical coarse-graining methods for upscaling/construction of mesoscopic models that are capable of capture long-time behavior using atomistic simulation data. The candidate will also develop integrated uncertainty quantification methods for dynamically downscaling/folding back to atomistic simulations when the quality of mesoscopic models deteriorates in the dynamical simulations.
What You Need
Minimum Job Requirements:
Knowledge in scale-bridging and model coarse-graining
Hands-on experience in modern statistical inference, data-driven methods, and machine-learning
Proficiency in probabilistic reasoning and uncertainty quantification
Proficiency in scientific computing in Python and c++ Willingness to learn/work on exciting and cutting-edge material science problems
Ability to conduct independent and collaborative research
Ability to organize and prioritize tasks under tight time constraints for effective achievements of project goals
Excellent interpersonal, oral, and written communication skills
Education/Experience: A PhD in Applied Mathematics, Condensed Matter or Statistical Physics, Material Science, Data Science/Machine Learning, or related fields completed within the last four years
Proficiency in TensorFlow or PyTorch
Strong track record in research publications
Ability to adapt to new requirements for projects and be flexible to learn new computational tools
Experience in applying data-driven and/or uncertainty quantification methods on either simulation or empirical datasets
Location: This position will be physically located in Los Alamos, New Mexico.
COVID Vaccine :
The COVID vaccine is mandatory for all Laboratory employees, on-site contractors, and on-site subcontractors unless granted an accommodation under applicable state or federal law. This requirement will apply to those working on-site, those teleworking, and all new hires.
Where You Will Work
Located in beautiful northern New Mexico, Los Alamos National Laboratory (LANL) is a multidisciplinary research institution engaged in strategic science on behalf of national security. Our generous benefits package includes:
§ PPO or High Deductible medical insurance with the same large nationwide network
§ Dental and vision insurance
§ Free basic life and disability insurance
§ Paid childbirth and parental leave
§ Award-winning 401(k) (6% matching plus 3.5% annually)
§ Learning opportunities and tuition assistance
§ Flexible schedules and time off (paid sick, vacation, and holidays)
§ Onsite gyms and wellness programs
§ Extensive relocation packages (outside a 50 mile radius)
Directive 206.2 - Employment with Triad requires a favorable decision by NNSA indicating employee is suitable under NNSA Supplemental Directive 206.2 . Please note that this requirement applies only to citizens of the United States. Foreign nationals are subject to a similar requirement under DOE Order 142.3A.
Position does not require a security clearance. Selected candidates will be subject to drug testing and other pre-employment background checks.