10 machine-learning research jobs at University of Minnesota Twin Cities in United States
Sort by
Refine Your Search
-
Listed
-
Field
-
. The researcher should have expertise in computational neuroscience (both neural modeling and machine learning expertise preferred), non-human animal behavioral neuroscience (rat, multi-site silicon probe neural
-
collaborating as a member of a research team. Experience with one or more of the following: machine learning, natural language processing, LLMs Create a Job Alert for Similar Jobs About University of Minnesota
-
employs machine learning methods to integrate high-dimensional multi-omics data, elucidating biological insights into aging and disease pathways. The successful candidate will specialize in statistical
-
. The successful candidate will use different computer programs to analyze viral neutralization assays and antibody binding. Analysis of flow cytometry results will be done using FlowJo or Cyflogic programs and
-
material for specialized analysis including PCR analysis. Experience processing and analyzing samples. Experience working with laboratory animals is desired, but not required. Basic computer skills are
-
. Proficiency in computer programming in R (preferred) or other languages. Demonstrated excellence in written, virtual, and in-person communication. Ability to work effectively with individuals (students
-
, health informatics, machine learning, and geospatial analysis to work with state public health and health system leaders. Position Overview We are seeking a candidate with training in data analytics
-
system hardware, and is also heavily involved in DUNE far detector construction and analysis, including the development of machine learning techniques to translate near detector events to the far detector
-
with detailed documentation, data analysis, and common computer applications Interpersonal skills necessary to communication effectively with team members and project collaborators Ability to collaborate
-
experiments (5%), batch experiments (5%), sample collection and parameter measurement (5%), experiment setup and operation (20%), data collection and recording (10%), sample analysis by wet chemistry and auto