Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
applications. Openness to hybrid .NET and Python ecosystems to support the evolving needs of our AI integration efforts. Strong understanding of modern data architectures (data lakes, lakehouses, vector stores
-
related field by the time of appointment. We are looking for candidates with: · Strong programming skills (e.g., R, Python, or C++) and experience with Linux systems · Expertise in Omics data analysis
-
, Python, etc.). Builds lightweight, human-centered tools that simplify complex data or systems for a range of users. Conducts informal user testing and gathers feedback from staff, students, and
-
. Openness to hybrid .NET and Python ecosystems to support the evolving needs of our AI integration efforts. Passion for supporting digital transformation through AI education and enablement across diverse
-
computational research projects is required for the proposed research. Expertise in population or evolutionary genetics is preferred but not required. Required skills: · Experience with Python · Experience with
-
optical systems and the use of lasers. Proficiency in data processing software e.g., Python, Matlab. Exhibits excellent professionalism and work ethics, initiative and self-motivation. Strong ability
-
skills: · Experience with Python · Experience with high performance computing clusters · Development of reproducible analysis pipelines · Strong time management and organizational skills · Ability to work
-
Knowledge of simulation medical high-fidelity simulators and systems (e.g., Laerdal and Gaumard) General knowledge of servers, databases, and web applications (e.g., Microsoft 0365, Python, HTML, and
-
. Experience in Python code, Matlab, and LabChart. Knowledge of cardiovascular physiology. Department Contact for Questions Miranda Lockwood, SHRM-CP Human Resources Business Partner mjlockwo@iu.edu Additional
-
, genotype data, biomarkers, etc.) is required. Desire to apply your skills in data analysis to utilize bioinformatics methods (e.g., from R/Bioconductor) or machine learning tools (e.g., using Python packages