Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- Sweden
- Germany
- Norway
- Denmark
- France
- Netherlands
- Spain
- Austria
- Belgium
- United Kingdom
- Poland
- Luxembourg
- Portugal
- China
- Singapore
- Canada
- Finland
- Czech
- Italy
- Romania
- Switzerland
- Cyprus
- Ireland
- Lithuania
- Saudi Arabia
- Slovenia
- United Arab Emirates
- Worldwide
- Andorra
- Brazil
- Bulgaria
- Hong Kong
- Japan
- Latvia
- Taiwan
- 26 more »
- « less
-
Program
-
Field
- Computer Science
- Biology
- Medical Sciences
- Science
- Engineering
- Economics
- Mathematics
- Humanities
- Chemistry
- Materials Science
- Linguistics
- Education
- Earth Sciences
- Physics
- Psychology
- Electrical Engineering
- Environment
- Law
- Arts and Literature
- Business
- Sports and Recreation
- Design
- Philosophy
- 13 more »
- « less
-
and automated floor-plan recognition, to fill data gaps and harmonise information from disparate sources. Learn more and watch our project video here: https://sb.chalmers.se/digital-material-inventories
-
on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
-
fields. Your research can be theoretical, applied, or span both. Deadline February 9, 2026. Where to apply Website https://jobrxiv.org/job/ellis-institute-finland-27778-postdocs-in-machine-learn
-
position as Senior Lecturer. Optimization, machine learning, and control theory together form a central toolbox for understanding, analyzing, and controlling complex systems. These fields span deep
-
join the group to develop AI and machine learning based software to assist clinical workflow and pre-clinical studies. Required Qualifications: Ph.D. in a physical science or engineering field Strong
-
software. (0-35) Experience in the application of advanced machine learning techniques (e.g., graph neural networks, reinforcement learning, probabilistic models, or latent representations) to biomedical
-
metaproteomics approaches Analyzing large-scale multi-omics and clinical datasets to investigate individual metabolic responses to diet. The work includes applying advanced statistical and machine learning methods
-
the application of these methods to problems in the physics of oxides, semiconductors, metals and their surfaces. Machine learning methods are used to close the complexity gap. Currently, the group consists
-
to imagine novel task configurations and learn robust manipulation policies from just a few real demonstrations. You will work at the intersection of 3D computer vision, physical simulation, and robot learning
-
of formulating them, incorporating their own ideas and experience in computer vision, machine learning, and related fields, to further visualization and interpretation of molecular images. Our research environment