Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
, autonomous learning agents are likely to take an active role in human society, engaging in daily interaction and collaboration with humans. Developing learning algorithms that enable these agents to produce
-
of Artificial Intelligence in Green Algorithms Research line / Scientific-technical services: Development of algorithms that are energy efficient Grant/funding period: START: 01/05/2024 END: 31/03/2027 Centre
-
. In particular they will play a leading role in algorithm emulator development and maintenance as well as coordinating the definition and testing of interfaces to the Level-1 Trigger system. It is
-
27.10.2025 Application deadline: 30.11.2025 Are you excited about the possibility to explore ethical, philosophical, legal, epistemic or social implications of using machine learning in different
-
methodology will involve the development of mathematical models for signal transmission and reception, derivation of fundamental performance limits, algorithmic-level system design, and performance evaluation
-
. For example, we would like to be able to track how the prevalences of different strains in a mixed sample change over time. Your role: You will develop and implement algorithms to find, quantify and track
-
. This role will contribute the development of novel machine learning algorithms for wireless sensing and communication and the proof of concepts for next-generation wireless communication, collaborating with
-
comparing models with entirely different structures and parameter counts, whether comparing linear regression against mixture models or decision trees. MML is strictly Bayesian, requiring prior distributions
-
silver artefacts. Specifically, we will seek to understand what detail is being missed, using current assaying approaches. The project will showcase what insights, at different length scales, could be seen
-
in mathematical theory. Unlike standard deep networks, each connection in a KAN learns a continuous function, allowing a richer and more flexible representation of computation. This perspective aligns