Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
of elements) of the model.; 3) develop an optimization algorithm based on genetic algorithms and metamodels and 4) design functionally graded OC scaffolds using different biomaterials. The doctoral candidate
-
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
-
title: UDC-Inditex Chair of Artificial Intelligence in Green Algorithms Research line / Scientific-technical services: Development of algorithms that are energy efficient Grant/funding period: START: 01
-
title: UDC-Inditex Chair of Artificial Intelligence in Green Algorithms Research line / Scientific-technical services: Development of algorithms that are energy efficient Grant/funding period: START: 01
-
score for candidates to be placed in the pool. The pool will list candidates in descending order of score. Candidates in the pool may be contacted if a successful applicant does not accept the position
-
with peer review system. Expertise in optimization software and algorithms as well as in Reliability Analysis (maximum 20 points). • Projects and academic/research works: 5 points/item • Author or co
-
, especially algorithms and computational complexity. Applied mathematics: computational mathematics, statistics and data science, physical mathematics, mathematical and computational biology, biostatistics
-
models, signal processing methods, artificial intelligence (AI) tools, and optimization algorithms grounded in electromagnetic (EM) principles. The project is inherently interdisciplinary, bridging
-
the stages of the recruitment process and, where applicable, passed the relevant test. In such a case, the panel may set a minimum score for candidates to be placed in the pool. The pool will list candidates
-
offer a PhD student position at the Universitat de Barcelona (as part of the PhD Program in Mathematics and Computer Science) to develop new AI and federated learning algorithms for diagnostic imaging in