22 machine-learning-"https:"-"https:"-"https:"-"CNRS" PhD positions at Stockholm University
Sort by
Refine Your Search
-
. Project description Machine Learning – Algorithms and Theoretical Foundations Recent advances in AI have highlighted the potential of learning systems that make sequential decisions and adapt to
-
. Project description Resource-Efficient On-Device AI/ML for IoT Systems This PhD project investigates resource-efficient multimodal machine learning for intelligent Internet of Things systems. It
-
) will work at the interface of structural bioinformatics, machine learning, and membrane biology, in close collaboration with experimental groups in membrane biology and structural biology. The Elofsson
-
the design, implementation, and evaluation of artifacts that integrate immersive experiences across the XR spectrum (augmented, virtual, and mixed reality) with real-time machine learning algorithms
-
solid experience in machine learning (including deep learning), ideally in the context of computer vision and, more broadly, a proven track record in scientific programming (GitHub Repository or
-
improve climate model accuracy, efficiency and generalizability, and leverage modern computer architecture. We will explore the use of machine learning (ML) models trained on combinations of
-
The Department of Ecology, Environment and Plant Sciences invites applications for a four-year PhD position in the framework of the project “Harnessing evolutionary transitions, machine learning, and
-
experiments, site-directed mutagenesis) and/or multiscale molecular simulations (QM/MM and atomistic molecular dynamics simulations, free energy methods, and machine learning). Candidates with either an
-
protein complexes. The systems are explored by multiscale molecular simulations (QM/MM and atomistic molecular dynamics simulations, free energy methods, and machine learning) that are tightly linked
-
, machine learning and data analytics, large-scale simulations, prototype implementation, and empirical evaluation in realistic environments. Particular emphasis will be placed on research that integrates