21 data-"https:"-"https:"-"https:"-"https:"-"Dr"-"UCL" Fellowship positions at University of Michigan
Sort by
Refine Your Search
-
, and research. The 884-bed medical center currently employs 2,700 faculty, cares for ~48,000 inpatients per year and provides approximately 2.3 million clinic visits and 54,000 surgeries per year. Data
-
research projects in computer vision, machine learning, AI, and robotics. Projects may include physically-grounded AI guidance agents, modeling of multimodal data, and generative AI systems for situated
-
, and in vivo mouse models, working in close partnership with the lab's computational team to generate data-rich spatial multi-omics datasets that drive discovery. As part of the Bioinnovations in Brain
-
. Responsibilities* Empirically characterize sand samples through shear testing, CT scanning CAD and 3D print models for sand intrusion Design and perform impact experiments. Collect data using high-speed video
-
. Must have experience with analysis of sequencing data (RNA-seq, CUT&RUN, etc) and/or sufficient computation background to learn these analyses. Must have a strong publication history supporting potential
-
of Michigan is committed to foster learning, creativity and productivity, and to support the vigorous exchange of ideas and information, not only in the classroom but in the workplace by: Creating a work
-
Apply Now How to Apply Interested individuals should include a cover letter, CV and the names and contact information for 2-3 professional references. Job Summary We are seeking an experienced
-
Information The salary range is $65,000 - $72,000. Please note a higher salary may be offered based on equity and the selected candidate's experience. Please note a higher salary may be offered based on equity
-
. What You'll Do The candidate will join a multidisciplinary team focused on accelerating drug discovery with data-rich and automated chemical methods. We are a medicinal chemistry lab with expertise in
-
meaningful information from noisy or ambiguous sensory signals? To what extent does neural plasticity in the auditory system occur via mechanisms dictated by classic theories of reinforcement learning, or does