11 machine-learning-phd Postdoctoral positions at University of Delaware in United States
Sort by
Refine Your Search
-
transistors, thin film transistors); (3) Light-emitting devices and high-speed detectors 2. Circuits: Analog circuits for Neuromorphic computing Qualifications: PhD required at the time of hire 1-year analog
-
of cybersecurity. Major Responsibilities Conduct high-quality research on the broader areas of cybersecurity, hardware security, privacy-preserving machine learning, and applied cryptography. Investigate
-
/ ) focused on phonological learning and speech production (PIs: Drs. Sayako Earle and Ho Ming Chow). This position is 100% research with an ample opportunity to develop teaching skill. The successful candidate
-
Postdoctoral Researcher in Jewish History for the academic year 2025-2026. Under the direction of the Jewish Studies Program Director, the Chaiken Postdoctoral Researcher will teach one course per semester in
-
rise (SLR) and flooding. Integrate field data (e.g., salinity, nutrient levels, soil and water properties) into the development of numerical models to enhance predictive accuracy. Apply machine learning
-
of computer simulations of system related to different replication stages of the HIV virus. Preference will be given to applications with expertise in 1) protein dynamics; 2) molecular dynamics simulations; and
-
must have experience with and knowledge of computer programming for the purpose of coding web-based valuation tools that can be used for benefit cost analyses and optimization approaches. The successful
-
resilience and climate change adaptation. A PhD in economics or applied economics (or closely related field) is required, with a preference for candidates specializing in environmental, agricultural, and
-
-THz instrument based on a commercial novel 25 fs, >3mJ laser system. • Conduct benchmark experiments on new magnetic systems in collaboration with the MRSEC CHARM center. QUALIFICATIONS: • PhD in
-
to advance the intersection of these fields requires extensive familiarity with experimental and computational analysis of soil communities, ecological theory, and familiarity with machine learning. We expect