We are looking for highly motivated individuals with a strong technical background in theoretical condensed matter physics and materials science combined with experience operating in a high performance computational environment afforded, for example, by DOE supercomputing facilities at Oak Ridge National Lab and Argonne National Lab.

The Condensed Matter Physics and Materials Science Division seeks an individual with a proven track record of research achievement capable of leading the new effort that complements existing research programs within the department. We are especially interested in candidates who, through their research, will contribute to the diversity and excellence of the research effort of CMPMSD.

Scientific Computing is a graduate course on research computing, covering techniques and methods for reliable and efficient computational programming. It includes best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing.

The School of Electrical & Electronic Engineeringisseeking a talented independent-minded and highly motivated Research Fellow (PhD) keen on theoretically and/or experimentally tackling fundamental questions in the study of light-matter interactions.Our project aims to conclusively demonstrate that the shaping of electron wavepackets can affect the shape of outgoing radiation; and to develop electron waveshaping as a versatile tool for enhancing the output of compact X-ray sources and single-photon sources.

Posting Duration: This posting will be open for application submissions for a minimum of seven (7) calendar days including the posting date.Sandia reserves the right to extend the posting date at any time.

Posting Duration: This posting will be open for application submissions for a minimum of seven (7) calendar days including the posting date.Sandia reserves the right to extend the posting date at any time.

Posting Duration: This posting will be open for application submissions for a minimum of seven (7) calendar days including the posting date.Sandia reserves the right to extend the posting date at any time.

Posting Duration: This posting will be open for application submissions for a minimum of seven (7) calendar days including the posting date.Sandia reserves the right to extend the posting date at any time.

Posting Duration: This posting will be open for application submissions for a minimum of seven (7) calendar days including the posting date.Sandia reserves the right to extend the posting date at any time.

Posting Duration: This posting will be open for application submissions for a minimum of seven (7) calendar days including the posting date.Sandia reserves the right to extend the posting date at any time.

The position is part of the NexGenTeN ERC project which focuses on the development and application of 2D tensor network algorithms for the accurate study of strongly correlated quantum many-body systems. Research areas include the development of 2D tensor network algorithms for ground states, spectral functions, finite temperature simulations, open systems, and multi-scale approaches, with applications in single and multi-band Hubbard models, frustrated magnets, SU(N) systems, open quantum systems, and other strongly correlated systems.

Posting Duration: This posting will be open for application submissions for a minimum of seven (7) calendar days including the posting date.Sandia reserves the right to extend the posting date at any time.

Applications are invited for appointment as Post-doctoral Fellow in Computational Condensed Matter Physics in the Department of Physics (Ref.: 512480 ) to commence as soon as possible for two years with the possibility of renewal.

Applications are invited for appointment as Post-doctoral Fellow in Computational Condensed Matter Physics in the Department of Physics (Ref.: 512480 ) to commence as soon as possible for two years with the possibility of renewal.

We seek to hire a PhD candidate to computationally investigate fundamental physical and chemical processes which occur due to application of low temperature plasmas to water.

This position is for a research and development position focusing on computational electromagnetic models within the finite-difference time-domain (FDTD) and beam propagation method (BPM) frameworks. The successful candidate will be responsible for developing models or tools within the applicable Optiwave products. Development of application examples representing the current state of literature developments will also be of interest.

You will be working within our Simulations & High Performance Computing Team, a team of engineers and scientists dedicated to develop massively parallel toolkits to deign superconducting quantum processors. You will focus on developing software tools for the design and simulation of quantum devices which span multiple scales, involve complex geometries, and incorporate a number of physical phenomena. You will collaborate closely with other teams when required. In this role, you will draw on your strong expertise as well as as your critical thinking and obsession for documentation.

The candidate should also have experience with multiple modern techniques and algorithms in machine learning and statistical computing such as regression, clustering/segmentation, time-series analysis, recommender systems, predictive analytics, Bayesian modeling, data fusion, supervised/unsupervised learning, decision trees, A/B testing, natural language processing.

Applications are invited for appointment as Research Assistant Professor in Computational Condensed Matter Physics in the Department of Physics (Ref.: 512689) to commence as soon as possible for two years with the possibility of renewal.

The position will be part of an integrated team of computational materials scientists, chemists, physicists and HPC application developers that will design and deliver novel approaches, methodologies, algorithms and software to tackle large scale ground and excited state first principles materials simulations and scaling efforts to future leadership class HPC Exascale architectures.