-
, Applied Mathematics, Computer Science, Statistics, Public Health, Economics or related computational field. Demonstrated experience in epidemiological methods, preferably using national mortality datasets
-
internationally) Requirements: The successful candidate should have the following qualifications: • PhD in Epidemiology, Data Science, Applied Mathematics, Computer Science, Statistics, Public Health, Economics or
-
, biological sciences, computer science, machine learning) Demonstrated quantitative skills, including proficiency in programming (R and/or Python) Previous experience developing statistical methods or working
-
Cornell University (5%). Qualifications: A Ph.D. in a relevant field (e.g. machine learning, ecology, biological sciences, computer science, statistics, engineering) Demonstrated quantitative skills
-
, operations research, or statistics). An ideal candidate would have proficient programming skills and demonstrated experience in computational modeling (particularly integrated assessment or power systems
-
operation, feedstock pretreatment and characterization, analytical measurements by LC and GC; experimental design, statistical analysis, bioprocess simulation through kinetic modeling. Additional training in
-
of Computing and Information Science under the direction of Principal Investigator Rene Kizilcec. The NTO is a collaboration among Cornell University, Carnegie Mellon University, and the Massachusetts Institute
-
this by producing scientific, computational, and big data resources that transform how we understand and protect the world around us. CESN engages the public by gathering biological information at large
-
environmental covariates (e.g., climate, land use, habitat change) with infectious disease dynamics using advanced statistical approaches analyzing bat movement data Collaborating with a transdisciplinary
-
modeling and risk assessment, and demonstrated experience in one or several of the following areas: Tropical cyclone dynamics and thermodynamics Statistical and/or machine learning approaches to weather