Sort by
Refine Your Search
-
tensor network algorithm development and application. Competency with common scientific programming languages such as C++, Python, and/or Julia and version control systems. Experience in parallel
-
robust procedures for sensor calibration, coordinate-frame management, timestamp synchronization, and data acquisition across heterogeneous devices. Develop and evaluate multi-sensor localization and
-
optimization Integrate real-time sensor data and telemetry into closed-loop control frameworks Test and validate control algorithms through simulation, hardware-in-the-loop testing, and/or physical testbeds
-
, machine learning, and statistical modeling methods for multimodal manufacturing datasets, including sensor streams, in-process signals, post-process characterization data, simulation outputs, and
-
and/or ab initio molecular dynamics simulations of liquids; (ii) advanced free energy simulation methods (e.g., meta dynamics, umbrella sampling); (iii) development and/or application of machine
-
analyzing interferometric sensor data such as extrinsic Fabry-Perot interferometry, optical frequency-domain reflectometry, optical time-domain reflectometry, or distributed acoustic sensing. Proficiency
-
, algorithm design, probability theory, privacy definitions, and apply it to develop efficient privacy preserved federated learning model. Communicate and coordinate experimental results with other
-
integrating phenotypic or imaging-based datasets (e.g., RGB, chlorophyll fluorescence, root imaging) with biogeochemical, microbial, or environmental sensor data. Experience analyzing large datasets
-
manufacturing, process physics, thermodynamics, and/or metallurgy to interpret sensor data in a materials science context. An excellent record of productive and creative research demonstrated by publications