21 learning-"https:"-"https:"-"https:"-"https:"-"https:"-"DAAD" positions at Zintellect
Sort by
Refine Your Search
-
and often different from the canonical types of data used to benchmark machine learning (ML) algorithms. In this opportunity, we will be evaluating how state-of-the-art ML techniques can be used to
-
aircraft (Cloud Physics Lidar and the Roscoe upper troposphere/lower stratosphere lidar). Additional projects include the development of machine learning and advanced data processing algorithms, and
-
atmospheric modeling, or related fields. Experience in machine learning techniques are highly desirable. Please see https://science.gsfc.nasa.gov/earth/mesoscale for more information about the
-
limited to): Implement machine learning transfer learning methods to homogenize AOD observations from the geostationary constellation of weather and atmospheric monitoring satellites (e.g. GEOS
-
project include (but are not limited to): Develop machine learning algorithms that utilize fire products from geostationary satellites to better represent fire evolution and variability Develop machine
-
, high-gain antenna systems, high-efficiency transmitters, low-phase noise RF sources, and other critical radar components. Topics also cover radar signal processing and machine learning, applying
-
Organization DEVCOM Army Research Laboratory Reference Code ARL-C-WMRD-300028 Description About the Research CCDC ARL Center for Agile Materials Manufacturing Science (CAMMS, https://www.arl.army.mil
-
, or machine learning approaches for handling big data in order to improve scientific insight and actionable information derived from NASA datasets. Candidates interested in leveraging multi-mission
-
neural operators and reduced-order models to approximate expensive forward and adjoint simulations while preserving underlying physics. Uncertainty-aware inference: combining physics-informed learning
-
evaluate encoder adaptations that incorporate both synthetic aperture radar and LiDAR-derived vegetation characterizations into multimodal geospatial learning pipelines. These capabilities will be