Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- United Arab Emirates
- Sweden
- Denmark
- United Kingdom
- Netherlands
- Morocco
- Germany
- Finland
- France
- Norway
- Poland
- Australia
- Belgium
- Canada
- Ireland
- Luxembourg
- Austria
- Japan
- Portugal
- China
- Spain
- Switzerland
- Taiwan
- Cyprus
- Hong Kong
- Singapore
- Brazil
- Iceland
- Israel
- Mexico
- South Africa
- 22 more »
- « less
-
Field
-
properties of Li-rich three-dimensional materials for lithium battery cathodes using density functional theory (DFT), molecular dynamics, cluster expansion, machine learning computational techniques. This work
-
one or more of the following areas is a BIG PLUS: data science (machine learning and AI), cancer biology, animal physiology, organic chemistry, E3-ubiquitin biology, and gene editing. In all cases
-
statistical or machine learning methods to complex datasets. Evidence of involvement in grant writing or development of independent research ideas. A commitment to teaching the next generation of researchers
-
employees and conducts research and teaching mainly in electrical engineering and computer technology. We are located on LTH's campus in northern Lund. At the Division of Electromagnetics and Nanoelectronics
-
the opportunity to: Acquire cutting-edge techniques for studying protein dynamics during ageing Explore fundamental biochemical mechanisms underlying the protein quality control of mitochondrially
-
in solid mechanics framework Experience in non-linear solid material response and fracture modeling Experience in machine-learning modeling for solid mechanics applications Experience in
-
include experience with fiber sensing, machine learning tools, and big data workflows. Instructions To apply, candidates will submit materials via Interfolio, comprising (1) a letter of interest describing
-
National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 5 hours ago
Earth observation (EO) data from NASA with state-of-the-art machine learning, we can produce a more accurate, dynamic, and actionable measure of wildfire risk. Project Goals and Objectives The primary
-
machine learning methods in the context of biological systems Experience with programming (e.g., Python, Perl, C++, R) Well-developed collaborative skills We offer: The successful candidates will be hosted
-
statistical or machine learning methods to complex datasets. Evidence of involvement in grant writing or development of independent research ideas. A commitment to teaching the next generation of researchers