Sort by
Refine Your Search
-
Category
-
Country
-
Employer
- Nanyang Technological University
- National University of Singapore
- University of Bergen
- AUSTRALIAN NATIONAL UNIVERSITY (ANU)
- Monash University
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- Universidade do Minho
- Center for Drug Evaluation and Research (CDER)
- Daphne Jackson Trust;
- Durham University
- Florida Atlantic University
- Hong Kong Polytechnic University
- Imperial College London
- Indiana University
- Macquarie University
- Sheffield Hallam University;
- UNIVERSITY OF SOUTHAMPTON
- UiT The Arctic University of Norway
- University of British Columbia
- University of California
- University of Otago
- Zintellect
- 12 more »
- « less
-
Field
-
applied to data-sparse regions of the globe. The successful candidate will have the opportunity to shape how these questions are tackled and will be expected to place their own stamp upon their work
-
structures and devices. Hands-on experience with optical characterization techniques. Experience with fabrication of nanomaterials, including electron-beam lithography, photolithography, dry etching, thin-film
-
pregnancies might still occur in women who are actively using or have recently discontinued GLP-1RAs. Currently, human data on safety outcomes associated with GLP-1RA exposure around conception are sparse
-
fabrication workflows involving lithography, dry etching, thin-film processing, metallization, release/quasi-release/release-free processes, and surface treatment. • Optomechanical applications: Contribute
-
to their large electro-optical coefficients and compatibility with nanoscale thin-film integration. In this project, we aim to use Density functional theory to calculate the -linear optics coefficients of doped
-
). Qualifications • Possess a recognized PhD degree in physics or nanoelectronics. • 4-5 years of experience in experimental device/spintronics research. • Experience in electrical transport measurements and thin
-
-processing workflows for single-photon LiDAR and time-resolved imaging, including event-based or neural-network-assisted approaches for efficient extraction of information from sparse photon measurements, low
-
, wearable sensors, or related device technologies. Hands-on experience with materials fabrication techniques such as electrospinning, solution processing, thin-film deposition or coating, printing, patterning
-
manufacturing, thin-film deposition, or polymer processing. Standards: Familiarity with ASTM, ISO, or industry-specific material specifications.
-
environments Utilizing Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models to augment sparse datasets and simulate edge-case operational and clinical scenarios