Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- United Kingdom
- Denmark
- Germany
- Sweden
- Belgium
- France
- Netherlands
- Luxembourg
- Morocco
- Norway
- Spain
- Hong Kong
- Italy
- Switzerland
- United Arab Emirates
- Australia
- Brazil
- Japan
- Worldwide
- Austria
- Benin
- China
- Finland
- India
- Israel
- Mexico
- New Zealand
- Poland
- Portugal
- Singapore
- Taiwan
- 22 more »
- « less
-
Field
-
. The project enables a targeted combination of expertise from machine learning, data science, and education with insights from educational practice. Drawing on large-scale data from authentic educational
-
the analysis of complex biomedical data using state-of-the-art AI and agentic system approaches, as well as the development of novel machine learning and deep learning algorithms. Your work will range from
-
candidate will have recently completed (or be close to completing) a PhD in Computer Science, Machine Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically
-
machine learning models. Working with extremely large, multi-modal datasets. Prior experience in analysis of clinical health records, and time series data are highly preferred. Qualifications Requirements
-
responsible for the design and testing of original machine-learning based methods for fetal heart biomarker discovery from the CAIFE image and video dataset. The full-time post is funded by InnoHK and is fixed
-
research, machine learning or artificial intelligence (e.g., large language models, EHR foundation models), causal inference (e.g., target trial emulation), and child health research. The research program
-
skilled in object-oriented coding (preferably Python) and data analysis; affinity with machine learning and explainable AI techniques, preferably in a geoscience context; good social skills. As a university
-
resonance imaging data among other imaging modalities, machine learning methods for prediction, treatment effect estimation, and contribute to understanding brain biomarkers of Alzheimer’s disease and their
-
of possible methodological components include self-supervised temporal representation learning for large volumes of unlabeled AE/electrochemical time-series data, switching state-space models that describe
-
and modelling of omics, clinical and imaging data, development of reproducible pipelines, application of machine learning techniques, integration of multi-modal data, scientific publication and