Sort by
Refine Your Search
-
Category
-
Program
-
Field
-
inventory, a major incentive for the project is the application and adaptation of state-of-the art machine learning codes to deal with redshift distortions, intrinsic (galaxy) biases, survey selection biases
-
of state-of-the-art machine learning potentials for multi-component alloy systems that are relevant for the new green steels compositions, including impurities and tramp elements. These models should enable
-
, ballistocardiography, and bio-radar) in combination with machine learning based algorithms for time series analysis into the whole OSA diagnosis and treatment pathway. During diagnosis unobtrusive sensors that can be
-
mechanics at the atomic scale. In this project, the University of Groningen will develop an array of state-of-the-art machine learning potentials for multi-component alloy systems that are relevant
-
adaptation of state-of-the art machine learning codes to deal with redshift distortions, intrinsic (galaxy) biases, survey selection biases and in particular the complications encountered in photometric
-
learning approaches and contributing to the development of better (bio)catalysts and drugs? We are offering three fully-funded, 4-year PhD positions at the University of Groningen or the Technical University
-
investigate how machine-learning based algorithms can be used to personalize the user experience. The goal of this personalized user experience is to enable each individual user to discover their own
-
implications. This PhD project offers a unique opportunity to work in an international environment in one of the biggest International Relations Department all over Europe and to acquire valuable research and
-
for this position will have the following qualifications/qualities A PhD degree in either machine learning or computational molecular sciences. Advanced knowledge in molecular machine learning. Advanced knowledge in
-
one of the biggest International Relations Department all over Europe and to acquire valuable research and teaching experience. The two main supervisors of the PhD project are Matteo CM Casiraghi and