28 machine-learning-"https:"-"https:"-"https:"-"https:"-"UCL" positions at University of Antwerp
Sort by
Refine Your Search
-
industrial Ph.D. position focused on developing scalable, Machine Learning (ML) pipelines for genomic and epigenomic biomarker discovery from Oxford Nanopore Technologies (ONT) long-read sequencing data
-
of Applied Engineering is looking for a full-time (100%) doctoral scholarship holder in the field of in-air acoustic sensing and applied machine learning for building the next-generation of intelligent robotic
-
work actively on the preparation and defence of a PhD thesis in the crossroads between the fields of robotics, signal processing and machine learning The candidate will explore how graph-based
-
), user interface design, or data visualization techniques. Familiarity with frameworks for explainable machine learning (e.g., SHAP, LIME, Captum, Alibi). Experience in designing context-aware, adaptive
-
communications and sensing, and a GPU Lab for training of advanced machine learning models. IDLab is both part of the University of Antwerp and the research centre imec. Position As a graduate teaching & research
-
. You can work in a group as well as on your own initiative. You have knowledge in machine learning for vision. Hands-on experience with image acquisitions and different types of cameras (visible
-
microscopy image interpretation), biological and medical sciences (neuroscience and brain microstructure), and computer science (machine learning and artificial intelligence) to achieve a breakthrough in
-
. You are fluent in Python, machine learning, and deep-learning tools (e.g., TensorFlow, PyTorch). You can speak and write fluently in English. A background in hydroclimate extreme event analysis is
-
to substantive political science questions. You have strong skills in automated text analysis and natural language processing (e.g., machine learning including neural networks, relation and entity extraction
-
. Kinetic rates will be calculated on the fly from molecular dynamics simulations using machine learning potentials. This approach will provide guidelines to steer the formation process of zeolites by tuning