Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- Sweden
- United Kingdom
- Germany
- Norway
- France
- Netherlands
- Denmark
- Belgium
- Hong Kong
- Australia
- Austria
- Singapore
- Poland
- Spain
- Switzerland
- China
- Italy
- Luxembourg
- Portugal
- Cyprus
- United Arab Emirates
- Estonia
- Canada
- Ireland
- Morocco
- Brazil
- Czech
- Finland
- New Zealand
- Saudi Arabia
- Barbados
- Europe
- Iceland
- India
- Japan
- Lithuania
- Taiwan
- Worldwide
- 29 more »
- « less
-
Program
-
Field
- Computer Science
- Medical Sciences
- Biology
- Engineering
- Science
- Economics
- Mathematics
- Psychology
- Humanities
- Chemistry
- Materials Science
- Linguistics
- Environment
- Education
- Social Sciences
- Business
- Electrical Engineering
- Physics
- Earth Sciences
- Arts and Literature
- Sports and Recreation
- Law
- 12 more »
- « less
-
, this approach needs to be reconsidered and adapted for the developing brain. To do so, we will use high-quality MRI data in a large cohort of infants (N~1000), including subgroups with clinical conditions. Our
-
either (computational) Bayesian methods, or statistical learning for complex data, unsupervised learning, or matrix factorization, is an advantage. Experience with management and analysis of large datasets
-
computational biology/chemistry, machine-learning for biological or chemical data, metabolism, and drug discovery/design. Mentorship is taken seriously and every effort will be made to ensure the candidate is
-
astrophysics (completed by the start date), demonstrated experience in large-scale structure simulations, working knowledge of applications of machine learning techniques in cosmology and/or astrophysics (in
-
sustainability issues. In particular, the “Probability/Optimization” group focuses on the theoretical understanding of algorithms used in machine learning, for training large neural networks and tuning
-
, medical informatics, databases, data mining, machine learning, applied mathematics, biomedical modelling and analysis of complex networks. Joint data science projects between the different partners
-
. The PhD will focus on two complementary approaches: 1) Enhancing CDI with machine learning: improve this technique using convolutional neural networks (CNNs) trained on simulated data, enabling faster and
-
incremental optimization. We seek researchers to develop next-generation machine learning methods that fundamentally rethink how large-scale AI systems are trained, fine-tuned, and deployed. Our focus is on
-
and Data-Driven Discovery, which involves creating a large, unique dataset linking composition to phase stability and fundamental mechanical properties for data-driven down-selection. The second pillar
-
at the intersection of educational data science, AI in education, and the learning sciences, with additional advisory support from faculty and researchers across learning sciences, computer science, machine learning