Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
possibly Understanding Statistics, Linear Algebra, and upper-level Data Visualization. Courses should be designed to support the success of students with diverse academic preparation and cultural and
-
scientific computing, control theory, data science, data driven methods, discrete mathematics, graph algorithms, high-performance computing, integral equations and nonlocal models, linear and multilinear
-
, 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
-
to Scientific Data Analysis Statistics for Scientific Data Analysis Calculus I & II for Physical Sciences Vector Calculus, Linear Algebra and Differential Equations Probability and Statistics Numerical Analysis
-
Experience and courses in one or more subjects are valued: machine learning, optimisation, linear algebra, deep learning, programming, dynamical systems. Rules governing PhD students are set out in the Higher
-
technology Strong mathematical education, in particular in relation to linear algebra Strong programming experience Ability to effectively communicate in written and spoken English Ability to work autonomously
-
linear algebra, computational methods, probability and statistics, data science, algorithms, artificial intelligence, operations research, and/or quantum optimization. The Department of Mathematics has a
-
, control theory, data science, data driven methods, discrete mathematics, graph algorithms, high-performance computing, integral equations and nonlocal models, linear and multilinear algebra, machine
-
, 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
-
can also be considered given that the candidate has formal competence in machine learning and/or image analysis/computer vision A solid and documented background in machine learning, mathematics, linear