Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Oak Ridge National Laboratory
- Texas A&M University System
- ETH Zurich
- Lulea University of Technology
- MOHAMMED VI POLYTECHNIC UNIVERSITY
- University of Twente
- Aarhus University
- Argonne
- Carnegie Mellon University
- King Abdullah University of Science and Technology
- NEW YORK UNIVERSITY ABU DHABI
- South Dakota Mines
- Technical University of Denmark
- University of Exeter
- University of Washington
- BioSpace
- Chalmers University of Technology
- Cranfield University
- EPFL
- Helmholtz Association of German Research Centres
- Linköping University
- Michigan State University
- Pennsylvania State University
- Singapore-MIT Alliance for Research and Technology
- Texas A&M AgriLife
- University of Manchester
- University of North Carolina at Chapel Hill
- University of Oklahoma
- University of Texas at Arlington
- Aarhus University (AU)
- CEA
- CSIRO
- Cornell University
- Duke University
- European Space Agency
- Florida Atlantic University
- Georgetown University
- Iowa State University
- NIST
- National University of Singapore
- Northeastern University
- Purdue University
- Queen Mary University of London
- Rochester Institute of Technology
- Rutgers University
- Semiconductor Laboratory of the Max Planck Society (HLL), Garching
- Stanford University
- Technische Universität Dresden (TU Dresden)
- Texas A&M AgriLife Extension
- Texas A&M University
- Texas A&m Engineering
- The Ohio State University
- The University of Arizona
- University of California, Los Angeles
- University of Central Florida
- University of New Hampshire
- University of New Hampshire – Main Campus
- University of Nottingham
- University of Southern Denmark
- University of Southern Mississippi
- University of Sydney
- Vienna University of Technology (TU Wien)
- Vrije Universiteit Amsterdam
- Yale University
- 54 more »
- « less
-
Field
-
the technical approach, explore AI-assisted inverse design and lead publications. Integration into a working sensor prototype will be a shared effort with the PI, PhD researchers and technical staff
-
of physical and cognitive function Conduct analysis of patient-reported and sensor-based quantitative data Epidemiologic principles and analytical approaches Advanced biostatistical methods Written
-
and visualisation (e.g. digital twins) at the interface between agricultural and biological sciences. This includes the integration of various types of field-based and sensor-based data (soil
-
digital, audiovisual, sensor, AI-supported, and other classroom and research technologies. Ability to work in classroom, office, and occasional outdoor school settings, including periods of sitting
-
digital, audiovisual, sensor, AI-supported, and other classroom and research technologies. Ability to work in classroom, office, and occasional outdoor school settings, including periods of sitting
-
and selectivity, shape the technical approach, explore AI-assisted inverse design and lead publications. Integration into a working sensor prototype will be a shared effort with the PI, PhD researchers
-
, imaging and sensor data to predict disease state, treatment response and the likely consequences of alternative clinical interventions. The position is funded until March 31st 2028 in the first
-
using diverse data sources, including field-collected biological samples, on-farm sensor data, artificial intelligence–derived outputs, and video-based analytics, to support disease surveillance and
-
projects, including the application of AI to solve underwater vision problems and Phase III of the Next Generation Sensor Systems for Natural Oceanic Bioluminescence Assessments. The Postdoctoral
-
the health impacts of extreme heat exposure, including building out data collection pipelines and leading the analyses of field data including survey, sensor, and wearable data. To work with faculty