Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- United Kingdom
- Germany
- Norway
- Sweden
- France
- Belgium
- Netherlands
- Denmark
- Spain
- Portugal
- Singapore
- Hong Kong
- Australia
- China
- Austria
- United Arab Emirates
- Poland
- Switzerland
- Canada
- Luxembourg
- Ireland
- Italy
- Czech
- Estonia
- Finland
- Latvia
- Romania
- Cyprus
- India
- South Africa
- Andorra
- Morocco
- Lithuania
- New Zealand
- Brazil
- Croatia
- Saudi Arabia
- Slovenia
- Worldwide
- Armenia
- Israel
- Japan
- Barbados
- Bulgaria
- Europe
- Greece
- Iceland
- Indonesia
- Malta
- Qatar
- Slovakia
- Taiwan
- Vietnam
- 44 more »
- « less
-
Program
-
Field
- Computer Science
- Medical Sciences
- Engineering
- Biology
- Economics
- Science
- Mathematics
- Business
- Chemistry
- Social Sciences
- Materials Science
- Arts and Literature
- Education
- Humanities
- Psychology
- Linguistics
- Electrical Engineering
- Environment
- Earth Sciences
- Law
- Physics
- Design
- Sports and Recreation
- Philosophy
- Statistics
- 15 more »
- « less
-
. The successful candidate will have the responsibility of developing, in collaboration with Dr Whelan and the PhD students, machine learning tools for the handling of the Mauve and MUSE datasets. They will also be
-
Qualifications: The successful candidate must hold a Doctorate/PhD degree or equivalent in machine learning or closely related field Experience with teaching on university level Strong background in machine
-
next-generation machine learning (ML) models that are both data-efficient and transferable, enabling more reliable catastrophic risk prediction, defined as the probability of exceeding critical safety
-
relational database environments Apply and evaluate methods from causal inference (e.g., confounding control, bias assessment, sensitivity analyses) Apply machine learning approaches for predictive modeling
-
., MATLAB, Python) is required. Experience with machine learning is highly preferred. Ability to work independently and as part of a team. Key Requirements for PhD: Hold a Bachelor's degree with outstanding
-
) approaches. Design predictive maintenance algorithms using machine learning, statistical learning, and digital twin-based models to anticipate failures and optimise maintenance interventions. Integrate AI
-
materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
-
of industrial processes. In a joint effort of both institutes, the Department AI4Quantum – Machine Learning for Quantum Simulation and Computing and Thermal Energy and Process Engineering are looking for a PhD
-
into reliable information about structural and aerodynamic behaviour remains a challenge. The PhD will develop data-driven methods that combine measurements, physics-based models, and machine learning to extract
-
Job Requirement Have relevant competence in the areas of Deep Learning/Computer Vision. The experience in diffusion models is a plus. Have a PhD degree in computer science/engineering or related