78 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "UCL" positions at KU LEUVEN
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
citation record must be focused on AI; or alternatively (B), machine learning engineers with an AI-focused PhD and demonstrated 2-year industry experience in AI development Applicants must have in-depth
-
the lifecycle of industrial systems. As machine learning sees broader adoption, companies are increasingly required to ensure the safety of machine-learning-enabled systems. The reliance on training data and the
-
children learn new words not only by listening to a storyteller but also by processing multimodal signals such as iconic gestures and gaze direction. Using eye-tracking in both real-life and digital contexts
-
how a novel machine learning-based methodology leveraging reinforcement learning with human feedback and multi-objective optimisation can be realized to generate new and even improve existing work plans
-
teacher education. Using Teach for All as a case-study, the project aims to better understand how and why education polices travel across time and space. While policy mobility is driven by a wide range of
-
the top 10% of their class in MSc and BSc, and have exceptional grades; should have a background in microwave engineering and machine learning; should have strong communication skills and be fluent in
-
research project. You will be given opportunities to participate at national and international meetings and establish your network. We offer a competitive salary (https://www.kuleuven.be/personeel/jobsite/en
-
, Mechanical), Computer Science, Applied Math/Statistics, Physics—or related. Candidates who will graduate in the near future are also welcome to apply. Strong foundation in machine learning/deep learning and
-
. Russell Friedman (russell.friedman@kuleuven.be ) Where to apply Website https://www.kuleuven.be/personeel/jobsite/jobs/60632449?hl=en Requirements Research FieldPhilosophyEducation LevelMaster Degree
-
such as surgery, patient or staff scheduling using, e.g., multi-objective optimization or machine learning approaches and analyzing efficiency-fairness trade offs. The research will be conducted under