31 phd-rehabilitation-engineering-computer-science Fellowship positions at University of Texas at Austin
Sort by
Refine Your Search
-
the last three years in Earth Science (e.g. Hydrology, Hydrogeology, Geology, Geography), Civil Engineering, or closely related field. Demonstrated experience and aptitude in a hydrology context with: (a
-
. Hydrology, Hydrogeology, Geology, Geography), Civil Engineering, or closely related field. Demonstrated experience and aptitude in a hydrology context with: (a) data analytics, wrangling, management, and
-
. Hydrology, Hydrogeology, Geology, Geography), Civil Engineering, or closely related field. Demonstrated experience and aptitude in a hydrology context with: (a) data analytics, wrangling, management, and
-
presentation, and manuscript preparation as part of a multidisciplinary team. Perform other related duties as assigned. Required Qualifications Doctoral degree, MD or PhD received within the past 3 years
-
, or free speech. Candidates should hold a PhD in a field such as History, Political Science, Public Policy, Criminology, Gender and Women’s Studies, Black Studies, Latino Studies, Ethnic Studies, Sociology
-
administrative tasks. Required Qualifications PhD in Clinical Psychology, Experimental Psychology, Neuroscience, or a closely related field (to be completed by the start date). PhD must have been received within
-
; original works by Frida Kahlo, including her iconic self-portrait with thorn necklace and hummingbird; the Gernsheim Collection, containing some of the world's finest examples of photographic art and science
-
symposium on campus that involves collaboration with other programs and units on campus, such as the Humanities, Health & Medicine Program, the Dell Medical School, and the College of Natural Sciences Present
-
/ epigenomics. The postdoctoral scholar will also join the PRC’s postdoctoral training program, which has associated professional development, mentoring, and capacity-building activities. What benefits do I
-
has also been developing physics-based machine learning algorithms for three dimensional seismic modeling, imaging and inversion using high performance computation including parallelization on GPUs