Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
Algorithmic Categorization”, 2024.07707.IACDC supported by measure “RE-C05-i08.m04 – “Support the launch of a program of R&D projects aimed at the development and implementation of advanced cybersecurity
-
, letters of recommendation, etc). What happens next? The assessment of potential candidates is made primarily based on academic results from bachelor degree and master degree studies. Short-listed applicants
-
of battery modelling and algorithm development, with a strong emphasis on the data-driven modelling and control aspects. You will contribute to shaping the technologies that underpin a more sustainable and
-
++, Python, and JavaScript languages, multi- and many-core SoC, RISC-V, hardware synthesis, hardware-software co-design, (meta-heuristic) optimization algorithms, machine learning frameworks, (bonus topics
-
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
-
particle physics and cosmology AI/Machine Learning Starting Date: 2025/12/05 Appl Deadline: 2026/01/05 08:59AM (posted 2025/12/04, updated 2025/12/02, listed until 2026/01/05) Position Description: Apply
-
to seamlessly integrate complex hormonal data, high-resolution wearable sensor streams, and endocrine test outcomes. Intelligent Artifact Detection: Develop cutting-edge Machine Learning algorithms
-
. The PhD position will focus on developing a deep-learning algorithm for analyzing the acquired experimental data. The PhD position will focus on development a comprehensive and AI-driven platform
-
or incomplete. Information Your tasks will include: Developing and benchmarking ML/AI algorithms tailored to low-data regimes — e.g. few-shot learning, transfer learning or data-efficient representation learning