Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
for Mathematics (KdVI) and the Informatics Institute (IvI), as well as Deep Blue Capital. This unique setting gives you the opportunity to combine rigorous academic research with real-world applications in
-
Vacancies PhD position on Dependability Driven on Device Learning Algorithms for Embedded Neuromorphic Architectures Key takeaways Edge devices that can learn autonomously while guaranteeing
-
student, you will be embedded in the research groups Computational Imaging and Deep Learning (CIDL) and Applied Quantum Algorithms (aQa), part of the Leiden Institute of Advanced Computer Science (LIACS
-
to the full development pipeline: from algorithm design and implementation to clinical integration and evaluation. You will also work on improving prognostic models using (neuro-symbolic) AI and develop
-
of the processing system online. Our approach will be to draw on a broad selection of tools including (deep) reinforcement learning, queuing networks, online algorithms and systems engineering. In addition, a large
-
master’s degree or equivalent in computer science, data science, AI, computational social science, or a related field. You have a keen interest in algorithms, ML, and AI, well-developed skill set in
-
Science, Information Science, Data Science and Artificial Intelligence. We employ over 200 people in four divisions: Artificial Intelligence & Data Science, Algorithms, Interaction, and Software. The atmosphere is collegial and
-
environment of the aQa (applied quantum algorithms) group, which is a team of faculty, postdoctoral researchers, and students across the Leiden Institute of Physics (LION), the Leiden Institute for Advanced
-
-based and index-based approaches, the sequent-peak algorithm, extreme value analysis, and multivariate copulas. Based on this, you will develop an improved method to map global energy drought risk and
-
. They can be constrained by either compute power or memory bandwidth. This information can be used to calculate the theoretical maximum energy efficiency of an algorithm that is run on an architecture