Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
-
Field
-
organismal domains and data modalities, making use of state-of-the-art methodologies such as systems/network analysis, artificial intelligence and machine learning and/or computational modelling approaches
-
to acquire larger scale research funding Proven methodological innovation capacities using network/systems analysis, machine learning, multimodal data integration or other areas relevant to the IBL Track
-
. Proven ability to acquire larger scale research funding Proven methodological innovation capacities using network/systems analysis, machine learning, multimodal data integration or other areas relevant
-
background in data sciences we ask: Insights in the most suitable data science techniques (e.g., machine learning, cluster analysis) to answer specific research questions based on available data as a basis for
-
areas across Europe presents a serious sustainability challenge. A modal shift towards cycling is required to achieve sustainable urban mobility, thereby reducing private car dependency, and improving
-
Essentials PhD (completed or near completion) in Computer Science, Computer Vision, NLP, Machine Learning, Computer Graphics/Animation, HCI, or a related field. Strong background in deep generative
-
Essentials PhD (completed or near completion) in Computer Science, Computer Vision, NLP, Machine Learning, Computer Graphics/Animation, HCI, or a related field. Strong background in deep generative
-
Essentials PhD (completed or near completion) in Computer Science, Computer Vision, NLP, Machine Learning, Computer Graphics/Animation, HCI, or a related field. Strong background in deep generative
-
University. Requirements A master’s degree in (applied) mathematics (or related), with a strong background in computational methods, preferably also using computational frameworks for machine learning in