Los Alamos National Laboratory · 12 hours ago
Data-driven and Machine Learning Postdoctoral Research Associate
Los Alamos, NM
Full-time
Onsite
New Grad, Entry LevelLos Alamos National Laboratory is a multidisciplinary research institution engaged in strategic science on behalf of national security. They are seeking a skilled researcher for a postdoctoral position focused on data-driven modeling of dynamical systems and machine learning, involving method development, theoretical analysis, and empirical validation.
Security
No H1B
U.S. Citizen Only
Responsibilities
Develop novel learning architectures that respect physical constraints and help discover underlying structure from data
Engage in method development, theoretical analysis, and empirical validation at scale on benchmark and mission-relevant datasets
Collaborate with a multidisciplinary team of mathematicians, physicists, and machine learning researchers
Participate in cross-disciplinary collaboration, scientific workshop organization, and conference participation
Qualification
Required
Experience in data-driven and/or ML methods for dynamical systems, as evidenced through a strong scientific record of peer-reviewed publications and presentations
Fundamental understanding of the Koopman and/or Perron-Frobenius Operators
Excellent scientific programming skills with demonstrated, hands-on experience (beyond online courses/certifications) using modern ML libraries and tools-e.g., PyTorch and/or JAX-as well as high-level languages such as Python (including NumPy/SciPy)
Strong mathematical training in at least one relevant field (e.g., functional analysis/operator theory, probability/stochastic processes, numerical analysis/scientific computing, and/or optimization/ML theory)
Ability to work both independently and collaboratively in an interdisciplinary environment
Ability to communicate technical results clearly in writing and presentations
Demonstrated creativity and interest in developing new research directions and original methodologies
PhD in Applied Mathematics, Computational or Statistical Physics, Applied Statistics, Computer Science, or a related field completed within the last 5 years or to be completed soon
Preferred
Prior research experience directly involving the Koopman and/or Perron-Frobenius operators
Prior research experience developing and/or implementing neural operators
Strong background in functional analysis/operator theory, including spectral theory, reproducing kernel Hilbert space methods, and the approximation of infinite-dimensional systems by finite-dimensional models
Experience with probabilistic modeling and uncertainty quantification (e.g., Bayesian deep learning, generative models, variational inference, ensembles, probabilistic scoring rules)
Experience developing novel neural network architectures (e.g., customized loss functions, complex network topologies, constrained or structure-preserving architectures)
Experience working with large numerical simulations or high-dimensional datasets and familiarity with high-performance computing environments (e.g., clusters, GPUs, job schedulers)
Benefits
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 (PTO and holidays)
Onsite gyms and wellness programs
Extensive relocation packages (outside a 50 mile radius)
Company
Los Alamos National Laboratory
Los Alamos National Laboratory, a multidisciplinary research institution engaged in strategic science on behalf of national security, is
Founded in 1943
10001+ employees
Funding
Current Stage
Late StageTotal Funding
unknownKey Investors
US Department of EnergyU.S. Department of Homeland Security
2023-08-16Grant
2023-05-19Grant
2023-01-17Grant
Leadership Team
Alex Delaney
R&D Engineer, Detonation Science and Technology
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