Sort by
Refine Your Search
-
opportunity to engage in cutting-edge interdisciplinary research focused on Generative AI within the context of construction robotics, working under the guidance of Dr. Arash Adel, Assistant Professor in
-
interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials
-
advanced microscopy techniques and related methods. Candidates who are nearing completion of their Ph.D. (i.e. with a confirmed defense date) or hold a Ph.D. in chemical engineering, chemistry, materials
-
position for new projects to characterize synthesis processes and novel materials in several research thrusts: i) development of advanced manufacturing processes for low-cost battery cathode active materials
-
The Ghanim lab is seeking a postdoctoral candidate or more senior researcher to study macromolecular complexes involved in LINE-1 retrotransposition using an integrated biochemical and structural
-
, cell biology, structural biology, microbiology, developmental biology, virology, genetics and cancer biology. The term of appointment is based on rank. Positions at the postdoctoral rank are for one year
-
on developing new systems models to examine social and biological drivers of infection inequality. The overarching goal of this postdoctoral position is to advance the use of mathematical and statistical models
-
vulnerability modeling, and (c) population and built environment exposure to climate hazards. The broad agenda of this research is assessing the fitness of geospatial indicators to inform conceptual and policy
-
retrotransposition using an integrated biochemical and structural approach with a focus on cryo-EM. The postdoctoral scholar will have access to cutting-edge cryo-EM instrumentation and computational resources through
-
interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials