Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- United Kingdom
- Sweden
- Germany
- Netherlands
- Norway
- Denmark
- France
- Belgium
- Australia
- Spain
- Austria
- Poland
- China
- United Arab Emirates
- Switzerland
- Portugal
- Singapore
- Hong Kong
- Canada
- Estonia
- Finland
- Luxembourg
- Czech
- Morocco
- Cyprus
- Italy
- Vietnam
- Brazil
- Greece
- Saudi Arabia
- Hungary
- Japan
- Latvia
- New Zealand
- Taiwan
- Worldwide
- 27 more »
- « less
-
Program
-
Field
- Computer Science
- Medical Sciences
- Engineering
- Biology
- Economics
- Science
- Mathematics
- Earth Sciences
- Environment
- Materials Science
- Business
- Education
- Psychology
- Chemistry
- Electrical Engineering
- Humanities
- Social Sciences
- Arts and Literature
- Linguistics
- Law
- Physics
- Sports and Recreation
- 12 more »
- « less
-
of the project, and will contribute to shaping the scientific directions of the AUTOMATIX project. Context The increasing availability of full-field experimental data and advances in machine learning offer new
-
heavily relies on empirical determination of key model parameters. By combining protein structure descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange
-
foundational neural models, where models learn from large unlabelled image datasets, but also on additional data like clinical reports or electronic health rec-ords. The work will be done in collaboration with
-
: 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
-
for Horticulture and Phenotyping) team research topics focus on low cost computer vision and machine learning, simulation assisted plant phenotyping and machine learning based data mining for plant biology
-
of written and spoken English. You should have experience with programming (e.g. Python, Julia), simulation methods (e.g. molecular dynamics) and modern machine learning methods. Additional Information
-
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
-
beneficial: Working knowledge of statistics and usage of MATLAB or other software for statistical analysis; Experience with machine learning and data mining. Good Estonian language skills Application procedure
-
skills and experience: Essential criteria PhD or equivalent (or thesis submitted*) in at least one of the following subjects: Computer Science, Machine Learning, Biomedical Engineering, Medical Imaging