Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- Singapore
- Australia
- United Kingdom
- Netherlands
- Germany
- Spain
- France
- Belgium
- Sweden
- Canada
- Norway
- Austria
- Portugal
- United Arab Emirates
- China
- Switzerland
- Italy
- India
- Denmark
- Poland
- Finland
- Ireland
- Japan
- South Africa
- Hong Kong
- Morocco
- Luxembourg
- Brazil
- Estonia
- Saudi Arabia
- Czech
- Macau
- New Zealand
- Cyprus
- Romania
- 26 more »
- « less
-
Program
-
Field
- Computer Science
- Economics
- Medical Sciences
- Business
- Engineering
- Science
- Biology
- Education
- Mathematics
- Arts and Literature
- Humanities
- Law
- Materials Science
- Social Sciences
- Linguistics
- Chemistry
- Environment
- Psychology
- Earth Sciences
- Sports and Recreation
- Philosophy
- Electrical Engineering
- Design
- Physics
- Statistics
- 15 more »
- « less
-
Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Research and implementation of model-merging techniques for deep
-
PhD studentship: Discovery of rational therapeutic biomarkers in breast cancer by systems pathology and deep learning Supervisor: Dr Hamid Raza Ali Department/location:Cancer Research UK Cambridge
-
interface of machine learning, deep learning, data science and applications in forest sciences. Together with the Director, you will further develop KIForst as a faculty-wide platform for methodological
-
Summary The research laboratory is seeking a motivated undergraduate student to assist with ongoing research projects in machine learning, deep learning, computer vision, and related areas
-
of computational pipelines and reproducible workflows for the analysis of biological and biomedical data using deep learning techniques; • promotion of technology transfer and support for the University's research
-
implement VLA models and world models. Develop and optimize deep learning algorithms to enable robotic arms to perform complex tasks guided by natural language instructions. Utilize PyTorch to train and fine
-
qualifications include: Strong research experience in deep learning and foundation models, including experience with pre-trained models, fine-tuning, transfer learning, or self-supervised learning. Experience with
-
dysbiosis drives immune dysregulation and disease progression in pediatric patients, generating new clinical multi-omics data and using deep learning, structural equation models, and causal inference
-
learning and physics, addressing key challenges in modern quantitative biology. The successful candidate will be responsible for: • Develop and train deep learning models (CNNs, ...) data to predict IPLSs
-
Consiglio Nazionale delle Ricerche-Istituto di Calcolo e Reti ad Alte Prestazioni | Italy | about 8 hours ago
techniques; Data preprocessing, feature selection, classification, and clustering; Programming languages and libraries for developing machine learning and deep learning applications; LanguagesENGLISHLevelGood