138 postdoc-distributed-algorithms positions at The University of Chicago in United States
Sort by
Refine Your Search
-
travel arrangements. Processes students, visitors, postdoc and faculty reimbursements through TEMS, ServiceNow and ePayment. Assists other members of the Office Staff as needed. Triages emails and
-
(if separate) to transition grant administration upon award, including updating proposed budget to match Notice of Grant Award. Interacts with University Research Administration, faculty, postdocs, and pre/post
-
/postdocs, and pays departmental invoices to vendors using GEMS card, Oracle, etc. Provides high-level events management for Institute colloquia, special seminars, and other events as needed by senior staff
-
programs with Unix / Linux commands. We also need someone who knows how to interface U2 with Visual Basic .NET Programming for new and existing applications. We distribute books for 130+ presses. Ideally
-
learning algorithms for a variety of predictive analytics research projects. Coordinates data collection, econometric analysis and provides quality assurance for research projects. Contributes to research
-
reimbursements. Reporting to the Academic Affairs Manager, the Lead Faculty Assistant also assists with distributing work across the Faculty Services team. This position will work on-site at least 4 days per week
-
, a person in this position will be a key contributor to the design and implementation of algorithms, AI/ML models, and workflows to enable the discovery of valuable information in large volumes of data
-
moderate level of guidance and direction. This position is expected to end on March 2027. Responsibilities Trains students and postdocs on computational genomic approaches. Serves as a resource for
-
experience in a large distributed computing environment. Previous experience in providing support for Linux HPC cluster used for scientific research. Technical Skills or Knowledge: Experience with installing
-
. Optimizes the performance and scalability of AI/ML workloads through algorithmic and system-level improvements, including evaluation and tuning of CPU vs. GPU usage for cost-effectiveness. Monitors and