62 gpu-computing Postdoctoral positions at Yale University in Ireland-University-Ranking-2024
Sort by
Refine Your Search
-
, or population genetics Deep learning for sequence, EHR, or imaging data High-performance and GPU computing environments Excellent candidates from adjacent quantitative fields are encouraged to apply. The Research
-
Computational Translation at Yale (IMPACT-Y) study. This large-scale, longitudinal project is designed to advance computational psychiatry by integrating mechanistically informed behavioral tasks with theory
-
Quantum Information Science and Engineering Theory Postdoctoral Associate Yale University: School of Engineering and Applied Science: Applied Physics: Yale Quantum Institute Location New Haven, CT
-
Yale Analog and RF Circuits and Systems (ARCS) Research Group, Department of Electrical and Computer Engineering, Yale University The Analog and RF Circuits and Systems Research Group in
-
The Yale CPC Training Program aims to train pre- and post-doctoral fellows in seven thematic areas: cancer etiology, cancer outcomes, lifestyle behavioral interventions, aging and cancer
-
The Yale Program for Recovery and Community Health (PRCH) does collaborative research, training, and policy development around behavioral health, aiming to support the recovery and social inclusion
-
The Breaker Laboratory at Yale University is seeking a highly motivated bioinformatician or computational biologist (Ph.D.) to join an interdisciplinary research team focused on the discovery and
-
spatial omics, longitudinal biomarkers, computer vision, electronic health data) with cutting-edge AI, we aim to fundamentally transform Parkinson’s disease from a disease without cures into a predictable
-
Postdoctoral Positions in the Yale Cancer Prevention and Control Training Program Yale University: School of Public Health Location New Haven, CT Open Date Feb 27, 2026 Deadline Feb 26, 2027 at 11
-
, validation, calibration, and inference. Working with large, longitudinal, structured and unstructured datasets in Linux and high-performance or GPU-accelerated computing environments. Applying rigorous methods