Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
PhD in Physics Strong background in quantum mechanics and linear algebra Research experience in quantum information or quantum foundations Publication record in peer-reviewed journals Experience with
-
(8) undergraduate courses in the academic year with four (4) in the fall semester and four (4) in the spring semester (lower and upper level such as Calculus, Discrete Mathematics, Linear Algebra, and
-
optimization, numerical linear algebra, partial differential equations, inverse problems, and large-scale stochastic systems. Faculty apply these strengths to critical fields including energy systems, medical
-
on the charged black hole by solving the linearized Einstein equations describing perturbations of the original black hole space-time. The source term in these linearized Einstein equations is the stress-energy
-
, which has significantly influenced its academic development. The successful candidate will: -Teach undergraduate and graduate mathematics and applied mathematics courses (such as calculus, linear algebra
-
which currently range from Basic Mathematics and Basic Algebra through Multivariable Calculus, Differential Equations and Linear Algebra. Minimum Requirements To perform this job successfully, an
-
, Preparation for Calculus, Calculus, and Linear Algebra. Adjunct faculty members will be required to use certain texts and meet certain parameters set by the Department of Mathematics. Adjunct instructors will
-
, tensor analysis, and network science to foster the professional development of team members. Qualifications and experience essential PhD in Applied Mathematics in the fields of Numerical Linear Algebra
-
courses in mathematics, including College Algebra, Precalculus, Calculus I and II, and Linear Algebra. The successful candidate will play a vital role in supporting student success, retention, and diversity
-
development spanning areas such as optimization, Fourier analysis, numerical linear algebra, statistics, machine learning, and high-performance computing for one or more of the following: (1) reconstruction