Sort by
Refine Your Search
-
individuals with interest and experience in numerical analysis, including water quality/isotope analysis and machine learning, and who have experience publishing academic papers in these fields. * Assigned
-
Inria, the French national research institute for the digital sciences | Bron, Rhone Alpes | France | 20 days ago
, “Learning the value of information in an uncertain world,” Nature Neuroscience, vol. 10, no. 9, pp. 1214–1221, 2007. A. Ferguson and J. A. Cardin, “Mechanisms underlying gain modulation in the cortex,” Nature
-
employ cutting-edge single-cell and spatial omics technologies with bioinformatics and machine learning to decipher principles of gene regulation underlying cell identity and its disruption in human
-
Associate with background on AI and machine learning for wireless networking and communications. The successful candidate will work under the direction of Dr. Marwan Krunz, Director of the Wireless
-
Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | 4 days ago
. [9]). We are particularly interested in improving the selection of transmission opportunities (e.g., using precomputed sequences), possibly constructed with machine learning techniques (as in [8]). We
-
, applications of machine learning to particle phenomenology, and lattice QCD, both within the Standard Model and beyond. The particle physics phenomenology group members are: J. F. Kamenik (head), B. Bajc, S
-
of Artificial Intelligence and Robotics at NYU Abu Dhabi the group of Prof. Kostas J. Kyriakopoulos seeks to improve the autonomy of Field Robotic systems by fusing control theoretic and machine intelligence
-
National Aeronautics and Space Administration (NASA) | Cleveland, Ohio | United States | about 3 hours ago
such as electrical power. Recent advances in machine learning present new opportunities to enhance the level of fault management and control in NASA's future power system applications. This work aims to
-
National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 3 hours ago
on the principle that by integrating high-resolution 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
-
research. This exciting position offers a unique opportunity to develop cutting-edge Knowledge Guided Machine Learning (KGML) methods informed by ecosystem modeling of agroecosystem carbon and nitrogen