Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- United Kingdom
- Germany
- Sweden
- France
- Norway
- Portugal
- Singapore
- Spain
- Netherlands
- Belgium
- Denmark
- United Arab Emirates
- China
- Italy
- Australia
- Canada
- Luxembourg
- Hong Kong
- Switzerland
- Austria
- Finland
- Poland
- Czech
- Ireland
- Estonia
- Japan
- Cyprus
- Latvia
- Brazil
- India
- Morocco
- Romania
- Saudi Arabia
- Lithuania
- South Africa
- Andorra
- Greece
- Taiwan
- Bulgaria
- Israel
- Slovenia
- Armenia
- Barbados
- Europe
- Iceland
- Malta
- Mexico
- New Zealand
- Slovakia
- Vietnam
- 41 more »
- « less
-
Program
-
Field
- Computer Science
- Medical Sciences
- Economics
- Engineering
- Biology
- Science
- Business
- Mathematics
- Education
- Psychology
- Materials Science
- Arts and Literature
- Chemistry
- Social Sciences
- Humanities
- Linguistics
- Earth Sciences
- Environment
- Sports and Recreation
- Law
- Electrical Engineering
- Physics
- Philosophy
- Design
- 14 more »
- « less
-
distributed wireless systems" which is conducted in collaboration between Linköping University (LiU) and Lund University (LU). Read more here: https://elliit.se/project/machine-learning-for-sensing-in
-
reconfigurable RF hardware for CAP-MIMO systems and contributing to machine learning-enhanced ISAC methods development through EM-informed modelling and hardware design. This is a unique opportunity to build
-
research into practical, scalable solutions for modern dairy farms. We develop machine learning models, decision-support tools, and digital platforms that improve production efficiency, herd health
-
Learning, or a closely related field. Strong understanding and demonstrated track record in protein structure modelling methods, with hands‑on experience in protein or biologics design and engineering. Hands
-
of Environmental Engineering, ETH Zürich and matriculate in ETH Zürich. The research is related to development of experimental and modeling techniques to identify emission sources, simulate the airborne transport
-
on the development of advanced artificial intelligence and machine learning methods for genome interpretation, with a particular emphasis on modeling the relationship between genetic variation and phenotypic outcomes
-
in modelling, simulation, or data analysis of energy systems Knowledge of machine learning or artificial intelligence methods Programming experience (e.g., Python, MATLAB or similar tools) Experience
-
landscape constrains or enables discovery. The project draws on tools from topological data analysis (e.g., persistent homology, Euler characteristic curves, discrete curvature), machine learning (e.g
-
datasets, modelling approaches, and performance metrics; develop physics-informed and data-efficient machine learning models to predict sorbent behaviour from sparse and multi-modal experimental data; and
-
bioinformatics for immunology research programs. You'll work at the cutting edge of AI-enhanced immunology, applying deep learning, foundation models, and advanced machine learning approaches to understand how