Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
questions within the theoretical and fundamental aspects of Mathematics, Physics, and Computer Science and Engineering, from various points of view and different abstraction levels. The selected candidate
-
and Boulton, 1968; Wallace and Dowe, 1999a; Wallace, 2005) is a Bayesian information-theoretic principle in machine learning, statistics and data science. MML can be thought of in different ways - it
-
questions within the theoretical and fundamental aspects of Mathematics, Physics, and Computer Science and Engineering, from various points of view and different abstraction levels. The selected candidate
-
programs. Alternatively, Mathematics, Computer Science, Computer Engineering, Electrical Engineering, or a similar field; Strong mathematical background: basic knowledge of graph theory and excellent
-
a similar field; Strong mathematical background: basic knowledge of graph theory and excellent background in linear algebra, finite fields and rings; Strong background in digital hardware design and
-
, or a similar field; Strong mathematical background: basic knowledge of graph theory and excellent background in linear algebra, finite fields and rings; Strong background in digital hardware design and
-
Quantum computing and Graph theory In this role, you will be responsible for conducting research on graph theoretic approaches to design quantum photonic experiments. Additionally, the position involves
-
addition to CSK from existing KBs) in targeted avenues such as multipurpose robots, along with mathematical modeling and algorithmic insights Counterbalancing issues such as bias, overfitting, and inexplicable
-
-based networks graph-based approaches Bayesian learning information theory Documented strong programming skills (preferably Python), for example with contributions to open-source projects, with an active
-
topics such as: neural networks self-supervised learning convolutional neural networks transformer-based networks graph-based approaches Bayesian learning information theory Documented strong programming