252 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"NOVA.id" positions at University of Pittsburgh
Sort by
Refine Your Search
-
Listed
-
Category
-
Field
-
Executive Director, University Center for Teaching and Learning Office of the SVC and Provost - Pennsylvania-Pittsburgh - (25006786) The Office of the Provost at the University of Pittsburgh (Pitt
-
Executive Director, University Center for Teaching and Learning Office of the SVC and Provost - Pennsylvania-Pittsburgh - (25006585) The Office of the Provost at the University of Pittsburgh (Pitt
-
Teaching Assistant/Teaching Associate/Teaching Professor in Human-Computer Interaction & Digital Narrative and Interactive Design SCI-Informatics and Networked Systems - Pennsylvania-Pittsburgh
-
must be able to teach Machine Shop, a one-credit laboratory course required for engineering students. The course introduces students to safe and effective operation of common machine tools, including
-
for live performance. Instruction combines lectures, demonstrations, and project-based learning with hands-on studio work. Students gain foundational skills in hand-sewing, machine operation, fabric and
-
Koenigshoff and Das labs. The Das lab has developed a range of interpretable machine learning approaches including SLIDE. This new position will focus on - i) applying machine learning and computational systems
-
, competing risks, longitudinal and repeated- measures models, causal inference, propensity-based methods, etc). Although experience with machine learning and generative AI is desirable, it is not required
-
samples, analysis of single-cell data generated by 10´Genomics platform, BD Rhapsody, etc., Integration of multiple datasets for the new discoveries using machine learning approaches, perform data analysis
-
The individual will develop and apply innovative machine learning methods for analyzing genetic, transcriptomic, proteomic, metabolomic, and clinical data from epidemiological cohorts, collaborate with a
-
, including developing quantitative and machine learning models using supervised and unsupervised techniques; Demonstrated experience leveraging high-dimensional player tracking or event data for player