Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
at the Faculty of Mathematics at TUD. Tasks: generation of hyper uniform patterns (point, scalar and vector fields) application of topological data analysis tools such as persistent homology and graph statistics
-
personalized mentorship from experienced professionals to accelerate your growth Collaboration. Work in an open environment that allows you to collaborate with multiple teams and get exposure to different groups
-
personalized mentorship from experienced professionals to accelerate your growth Collaboration. Work in an open environment that allows you to collaborate with multiple teams and get exposure to different groups
-
. The research group led by Martin Enge is specialized in methodology-driven analysis of patient data, especially in the field of single-cell multiomics. We are a multidisciplinary group with expertise in both dry
-
one of the following analysis techniques (multiple preferred): normative modelling, dimensionality reduction techniques, machine learning, deep-learning, state space modelling, advanced statistics
-
, localization, and sensing, with a focus on developing next-generation multiple-antenna systems while optimizing overall system performance. As a doctoral student, you devote most of your time to doctoral studies
-
for mathematics and computer science and is part of the Institutes Organisation of the Dutch Research Council (NWO) . The mission of CWI is to conduct pioneering research in mathematics and computer
-
perception, decentralization and mission execution. The RAI team has a strong European participation in multiple R&D&I projects, while RAI was also participating in the DARPA SUB-T challenge with the CoSTAR
-
Attributes and Behaviour Ability to negotiate and prioritise multiple, competing responsibilities and to work to deadlines Be open to new ideas, and welcome different perspectives and new ways of thinking
-
”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case