Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
Ine-Therese Pedersen 15th August 2025 Languages English Norsk Bokmål English English PhD position in Deep Learning for Metocean Data Apply for this job See advertisement About us We are announcing a
-
Apply now The Faculty of Science and Leiden Institute of Advanced Computer Science (LIACS) are looking for candidates for a: PhD in Deep learning for Electron Microscopy pipelines (1.0 fte) As a PhD
-
group has implemented state-of-the-art deep learning for underwater communications; deep learning models underwater environment based on real data. Our preliminary study shows that state-of-the-art deep
-
generative models, AI/ML, polymers, and/or materials science Documented track record of research in the area of the project What you will do Design and implement deep learning (DL) workflows for learning from
-
: Knowledge in deep generative models, AI/ML, microscopy, and/or molecular design Documented track record of research in the area of the project What you will do Design and implement deep learning (DL
-
/ Ecology Appl Deadline: 2025/07/31 11:59PM (posted 2025/05/21, listed until 2025/07/31) Position Description: Apply Position Description The Cordes Lab in the Department of Biology at Temple University is
-
system designed to safeguard underwater infrastructure. This is a unique opportunity to dive deep into advanced robotics research, gain hands-on experience with real-world testing, and make your mark in a
-
(or equivalent) in an appropriate discipline. Ideal candidate will have some prior knowledge in deep learning and computer graphics. Subject area: Medical imaging, biomedical engineering, computer science & IT
-
of the processing system online. Our approach will be to draw on a broad selection of tools including (deep) reinforcement learning, queuing networks, online algorithms and systems engineering. In addition, a large
-
to frailty assessment could be beneficial. Manual measurements from CT scans, however, are labor-intensive and subject to observer variability. The advent of deep learning in medical imaging presents a