Postdoctoral Positions Posted by Non-CNLS staff in areas related to CNLS research interests

CNLS Related Job Ads

The job number for each ad is clearly indicated as IRC*****. In order to apply follow these steps:

1. Open the following web page: http://www.lanl.gov/careers/career-options/jobs/index.php
2. Click on the upper right orange box that says Apply Now
3. In the new page search the appropriate job ad:
   In the Vacancy Name/Keywords box enter the appropriate code, IRC followed by the 5-digit job number (e.g. IRC59847)
4. Follow the instructions to complete your application

Job Ads

• IRC59834: Modeling and Simulation of Turbulent Reacting Flow
• IRC59425: Electronic Structure and Dynamics of Strongly Correlated Materials
• IRC59848: Infrastructure Optimization, Control, and Machine Learning
• IRC60864: Computational Biology Postdoctoral Researcher

Job Descriptions

• Modeling and Simulation of Turbulent Reacting Flow
  • Job ID: IRC59834
  • PI: Daniel Israel and Marianne Francois
  • Group: XCP-4
  • Description: The research activities will include developing theoretical turbulence models for variable density flow, developing numerical methods for compressible multiphase reactive flows and doing verification and validation. The successful candidates are expected to work in a team environment, to implement models and methods in large production software and to publish their research
  • Posted 12/7/2017
  
  
• Electronic Structure and Dynamics of Strongly Correlated Materials
  • Job ID: IRC59425
  • PI: Jianxin Zhu
  • Group: T-4
  • Description: The Group of Condensed Matter and Complex Systems in the Theoretical Division of Los Alamos National Laboratory (LANL) has an immediate postdoctoral position opening in the area of theoretical modeling and simulation of strongly correlated electronic materials. This position provides an opportunity to pursue fundamental studies of electronic structure and magnetic properties in heavy fermion and related materials. The postdoctoral project will involve applications of electronic structure methods such as the combination of local density approximation (LDA) with dynamical mean-field theory (DMFT) for spectroscopic analysis and understanding.
  • Posted 12/7/2017
  
  
• Infrastructure Optimization, Control, and Machine Learning
  • Job ID: IRC59848
  • PI: Russell Bent
  • Group: A-1
  • Description: The Advanced network Science Initiative (ANSI) is looking for outstanding postdoctoral researchers in the fields of applied mathematics, statistics, computational science, operations research, electrical engineering, and control theory. The research will focus on solving difficult optimization and control problems for the design, operations, simulation, and analysis of complex infrastructures systems. The candidate is expected to work in an interdisciplinary environment with significant collaborative research. A successful candidate is expected to publish in peer reviewed journals, as well as present their research at conferences and workshops.
    ANSI performs basic and applied research focused on modeling and understanding the nation's critical infrastructures, such as electric power and natural gas. Our expertise covers statistics, stochastic methods, machine learning, control theory, dynamical systems, discrete and continuous optimization, statistical physics, and graphical modeling. We ensure the scientific and technological validity of our approaches by working closely with physicists, engineers, mathematicians, statisticians, computer scientists, and economists through interdisciplinary collaboration. Our customers include DHS, DOE, DOD, and other national laboratories. Our work typically requires close collaborations within and outside the Laboratory.
    This is a two-year position. We are expecting to hire 3-4 postdoctoral researchers to support one or more of the following key project areas
    . Resilient Networked Microgrids: This project is focused on developing new optimization methods to design microgrids for resilient operations during extreme events and N-1 security over multi-year time horizons
    . Mixed-Integer Non-convex Optimization: This project is focused on developing new optimization techniques to solver non convex optimization problems
    . Natural Gas System Operations: This project is focused on developing new techniques to control and optimize the dynamics and transient behavior of natural gas systems. Areas of research include PDE-constrained optimal control, mixed-integer nonlinear optimization, variational analysis, computational inverse problems, and large-scale data analysis.
    . Distributed Optimization: This project is focused on developing scalable distributed optimization algorithms for continuous convex and non-convex optimization problems
    . Machine Learning for transmission and distribution power grid: This project is focused on developing new algorithms and methods for network reconstruction, dynamic state estimation and frequency events localization in power grids.
  • Posted 12/11/2017
  
  
• Computational Biology Postdoctoral Researcher
  • Job ID: IRC60864
  • PI: Thomas Liener
  • Group: T-6
  • Description: The successful candidate will develop models connecting evolution and epidemiology with the ultimate goal to predict and prevent human epidemics. Many viruses evolve rapidly and differently during various modes and rates of spread. This project will elucidate how the genetic evolution is linked to different spread patterns. The main virus to be studied is HIV, but other pathogens may become included in the project. We are looking for candidates with expertise in computational biology, evolutionary biology, and/or phylogenetics with interests in mathematical epidemiology.
  • Posted 12/12/2017