Ashkbiz Danehkar received his Doctor of Philosophy degree in Physics and Astronomy in 2014 from Macquarie University in Australia for his research on planetary nebulae around Wolf-Rayet central stars. He had been a doctoral candidate at Macquarie University's Research Centre in Astronomy, Astrophysics, and Astrophotonics in Sydney, Australia, since 2010. For his research, he conducted photoionization modeling, plasma diagnostics, elemental abundance analysis, and morpho-kinematic modeling of ionized nebulae. In his research project, he also used the Wide-Field Spectrograph (WiFeS) on the Australian National University's Advanced Technology 2.3-m Telescope at Siding Spring Observatory to observe gaseous nebulae. To perform 3D photoionization computations, he employed high-performance computing (HPC) resources on the supercomputer Vayu over 2010–2012 and Australia's first petaflop supercomputer, Raijin, available in 2013, from the National Computational Infrastructure (NCI) at the Australian National University (ANU) in Canberra. An international Macquarie University Research Excellence Scholarship (iMQRES) supported his doctoral research at Macquarie University.

Prior to his doctorate research, he obtained his Master of Science degree in Plasma Physics with distinction in 2009 from Queen's University Belfast in the United Kingdom after completing his research project on the propagation of electron-acoustic waves in a plasma containing suprathermal electrons and the postgraduate courses on theoretical and experimental plasma physics. A postgraduate studentship from the Department for Employment and Learning (DEL) in Northern Ireland supported his studies at Queen's University Belfast. In 2008, he also worked as a visiting early-stage researcher in theoretical and mathematical physics at the University of Craiova in Romania, funded by the Marie Curie Research Training Network, where he studied consistent interactions between dual linearized gravity (Curtright field) and a topological background field (BF) model in 1+4 dimensions. In 2007, he received his Master of Science degree in Computational Engineering, specializing in Electrical Engineering, with a good overall grade from the University of Rostock in Germany. There, he majored in electrical engineering and computational science, and his master's project focused on developing a microcontroller-based embedded measurement system for clinical practice. 

After completing his doctorate research, he began working as a postdoctoral fellow at the Harvard-Smithsonian Center for Astrophysics in Cambridge, Massachusetts (2015–2018), where he studied Chandra grating spectroscopic observations of X-ray ultra-fast outflows in active galactic nuclei and conducted X-ray photoionization modeling and spectral analysis. He also studied Hubble Space Telescope images of ionized nebulae and carried out Bayesian statistical analysis of collisionally ionized thermal plasma emission in X-ray observations of symbiotic stars. He then was a research fellow at the University of Michigan in Ann Arbor, Michigan (2019–2021), where he ran hydrodynamic simulations, along with collisional and non-equilibrium ionization computations, to describe the formation of X-ray superbubbles and the ionization structures of radiatively cooling superwinds in starburst regions of star-forming dwarf galaxies.

From 2022 until 2025, he worked as a research scientist at Eureka Scientific, Inc. in Oakland, California, thanks to NASA grants. His research included the spin measurement of supermassive black holes in active galactic nuclei (AGN), tidal disruption events (TDE), and hydrodynamic simulations aimed at the formation of Fermi γ-ray and eROSITA X-ray bubbles on both sides of the Milky Way.

Ashkbiz Danehkar is now a space scientist specializing in astronomy at the Science and Technology Institute (STI) of the Universities Space Research Association (USRA) in Huntsville, Alabama. In this role, he applies his expertise in astrophysics to assist NASA's Interagency Implementation and Advanced Concepts Team (IMPACT) with AI Foundation Models (FM) and Large Language Models (LLM), aiming to enhance the accessibility of NASA's mission data for the scientific community.