Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- United Kingdom
- France
- Germany
- Portugal
- Sweden
- Netherlands
- Spain
- Norway
- Belgium
- Denmark
- Italy
- Singapore
- Australia
- Czech
- Morocco
- Finland
- Ireland
- Luxembourg
- Canada
- Switzerland
- Austria
- Poland
- China
- Romania
- United Arab Emirates
- Estonia
- Japan
- Brazil
- Hong Kong
- Andorra
- Vietnam
- Barbados
- Bulgaria
- Latvia
- Lithuania
- Malta
- Worldwide
- 28 more »
- « less
-
Program
-
Field
- Computer Science
- Engineering
- Medical Sciences
- Biology
- Economics
- Science
- Materials Science
- Mathematics
- Earth Sciences
- Chemistry
- Environment
- Business
- Humanities
- Psychology
- Linguistics
- Law
- Physics
- Arts and Literature
- Electrical Engineering
- Social Sciences
- Sports and Recreation
- Education
- Philosophy
- Statistics
- 14 more »
- « less
-
these models for scalable vision tasks, instance segmentation, tracking, classification, and more. You will utilize probabilistic models to produce uncertainty-aware predictions across scales. This role requires
-
methods to understand and predict the adsorption, self-assembly, and protective behavior of N-heterocyclic carbenes (NHCs) on metallic and oxidized surfaces. NHCs are promising corrosion-inhibiting
-
of Robotics and Industrial Informatics (CSIC-UPC) offer a position to work on World Models for Human Behaviour Anticipation https://ramonllull-aira.eu/archivos/theme_field/world-models-for-human-behaviour
-
and simulation techniques, prior distributions and posterior predictive checks, model comparison, programming in R (python/Matlab), implementations using R-packages rstan/JAGS and brms/STAN
-
so that we can improve the prediction, diagnosis, prevention and treatment of common diseases such as Alzheimer?s, cancer and cardiovascular disease. We take a computational approach focused on
-
. This role focuses on developing and applying AI and deep learning techniques for analyzing high-dimensional omics data, identifying predictive biomarkers, and understanding cancer heterogeneity. Projects
-
learning methods (e.g., predictive modelling, clustering, multivariate integration) to large-scale time series and sensor datasets. Contributing to the development of risk models and decision-support tools
-
population changes, and other demographic parameters (survival, fecundity, reproductive success, etc.). These integrated population models (IPMs) are increasingly used in ecology. They offer clear advantages
-
-guided) Evolutionary trajectory analysis and fitness landscape modeling Integration of predictive algorithms with experimental iteration cycles High-throughput screening and selection platform development
-
, and clinical data. - Apply machine learning and foundational modeling to support predictive or exploratory analyses. - Collaborate with interdisciplinary teams to refine multi-modal pipelines