Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- Sweden
- United Kingdom
- Germany
- France
- Norway
- Portugal
- Singapore
- Belgium
- Spain
- Netherlands
- Denmark
- China
- Italy
- Switzerland
- Australia
- Canada
- Luxembourg
- Hong Kong
- United Arab Emirates
- Austria
- Finland
- Poland
- Czech
- Morocco
- Ireland
- Cyprus
- Japan
- Brazil
- Latvia
- India
- Saudi Arabia
- Lithuania
- Bulgaria
- Estonia
- Greece
- Taiwan
- Andorra
- Israel
- Romania
- Slovenia
- South Africa
- Armenia
- Barbados
- Europe
- Iceland
- Malta
- Mexico
- New Zealand
- Slovakia
- Vietnam
- 41 more »
- « less
-
Program
-
Field
- Computer Science
- Medical Sciences
- Economics
- Engineering
- Biology
- Science
- Business
- Education
- Mathematics
- Psychology
- Materials Science
- Arts and Literature
- Humanities
- Social Sciences
- Chemistry
- Linguistics
- Earth Sciences
- Environment
- Law
- Sports and Recreation
- Electrical Engineering
- Physics
- Design
- Philosophy
- 14 more »
- « less
-
involve developing an approach that uses Knowledge Organization (KO) metadata and ontologies to optimize parallel processing and scheduling policies (via Kubernetes) for Machine Learning tasks. The fellow
-
Instrument for Magnetic Sounding (PIMS) on the Europa Clipper Mission. Space Sci Rev 219, 62 (2023). https://doi.org/10.1007/s11214-023-01002-9 3. Kataoka, R., Nakano, S. & Fujita, S. Machine learning emulator
-
physics-informed machine-learning models for binding affinity predictions in rational small-molecule drug design. The models will allow prioritisation of candidates from hit discovery through to lead
-
outcomes. Key Responsibilities Develop, implement, and optimise AI/ML models (artificial intelligence/classical machine learning, deep learning, computer vision, NLP, etc.) Work with structured and
-
Architecture Search (NAS) that can automatically design efficient deep learning models optimized for specific embedded hardware platforms. These models will be deployed in resource-constrained, standalone
-
analyses. Machine learning for biological data (e.g., protein language models, transformers, generative models) and interest in building interpretable tools for experimental colleagues. Qualifications PhD
-
practical applications, including solving mathematical reasoning problems. The ideal candidate has a strong background in machine learning and an interest in bridging rigorous theoretical insights with
-
: comparative omics, genetic diversity analysis, mathematical modelling, machine learning, and the use of model organisms. Develop transferable skills such as scientific communication, project management
-
; Independent/collaborative development and deployment of common machine learning (ML) models; Data visualization using software (Tableau, Power Bi); Formal training/professional experience using relational
-
for showcasing the improved mapping and monitoring of forest traits and uncertainties. You will be mainly in charge of: Develop improved hybrid model inversion methods with a focus on machine learning and deep