Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- United Kingdom
- Sweden
- Netherlands
- Germany
- Norway
- Denmark
- Spain
- Belgium
- France
- Australia
- Austria
- China
- United Arab Emirates
- Switzerland
- Portugal
- Hong Kong
- Singapore
- Luxembourg
- Canada
- Finland
- Czech
- Poland
- Estonia
- Morocco
- Cyprus
- Greece
- Italy
- Vietnam
- Brazil
- Saudi Arabia
- Japan
- Latvia
- New Zealand
- Taiwan
- Worldwide
- 26 more »
- « less
-
Program
-
Field
- Computer Science
- Medical Sciences
- Engineering
- Biology
- Science
- Economics
- Mathematics
- Education
- Business
- Earth Sciences
- Environment
- Psychology
- Materials Science
- Chemistry
- Electrical Engineering
- Humanities
- Social Sciences
- Arts and Literature
- Linguistics
- Law
- Physics
- Sports and Recreation
- 12 more »
- « less
-
, reproducible tools and datasets. • Infrastructure and benchmarking for large-scale social-science simulation and validated workflows. The group website is https://torrvision.com/ Feel free to add Professor
-
Exciting and high-profile interdisciplinary research on visualisation, machine learning, and human-computer interaction Comprehensive computer infrastructure for AI and the analysis of large data volumes A
-
software engineering, computer science, data science, bioengineering, bioinformatics, engineering, physics or related Experience in either machine learning or computational biology. Interest in both
-
aimed at high-impact journals We are looking for the following qualifications: A PhD degree in a relevant field (machine learning or equivalent computer sciences) Experience in machine learning applied in
-
Functional Theory (DFT), machine-learned force fields (MLFF), graph neural networks (GNNs), or large language models (LLMs). Extensive Knowledge In: • First-principles atomistic simulations with packages
-
the reference number 27697, via our online portal: Apply now via https://jobs.uksh.de/job/Kiel-PhD-%28mfd%29-Statistical-Genetics-Machine-Learning-Schl-24105/1279933701/ For more information visit: www.uksh.de
-
of Helsinki. The main research fields at the department are artificial intelligence, big data frameworks, bioinformatics, data analysis, data science, discrete and machine learning algorithms, distributed
-
: 10.1101/2025.09.08.674950), and AI/machine learning. We work closely with clinicians to translate our findings into clinical practice, focusing on genomically complex sarcomas and haematological
-
been revolutionized in recent years by machine learned interatomic potentials (MLIP), and questions that were impossible to tackle five years ago can now be addressed. The state-of-the-art approach
-
Scene Understanding Detection and Identification of Objects (SSUDIO) project. The purpose of this project is to develop scene understanding from 3D scans of ships by applying machine learning/computer