Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- United Kingdom
- Singapore
- Australia
- Germany
- Netherlands
- Spain
- Sweden
- France
- Austria
- Portugal
- United Arab Emirates
- Denmark
- Norway
- Belgium
- Canada
- China
- Switzerland
- Ireland
- Italy
- Czech
- Poland
- Morocco
- Finland
- Romania
- Hong Kong
- Japan
- South Africa
- Cyprus
- India
- Luxembourg
- New Zealand
- Saudi Arabia
- Taiwan
- Armenia
- Croatia
- Estonia
- Latvia
- Lithuania
- Malta
- Slovenia
- Vietnam
- 32 more »
- « less
-
Program
-
Field
- Computer Science
- Economics
- Medical Sciences
- Business
- Engineering
- Science
- Biology
- Education
- Arts and Literature
- Mathematics
- Social Sciences
- Humanities
- Materials Science
- Chemistry
- Earth Sciences
- Environment
- Law
- Psychology
- Linguistics
- Sports and Recreation
- Electrical Engineering
- Design
- Philosophy
- Physics
- 14 more »
- « less
-
University of Massachusetts Medical School | Worcester, Massachusetts | United States | about 10 hours ago
expose the successful candidate to cutting-edge genome editor engineering approaches and the delivery of these reagents in vivo via AAV or lipid nanoparticles. The successful candidate will also learn
-
evaluate deep learning models for MRD detection and characterization Collaborate with multidisciplinary teams across Dana-Farber Cancer Institute, the Broad Institute, and more Mentor and guide junior staff
-
closely related quantitative discipline. Demonstrated experience with large-scale deep learning models and modern ML frameworks (e.g., PyTorch, JAX, Transformers), including training, fine-tuning
-
learning, particularly deep learning and physics-informed methods, offer transformative opportunities to redesign how data are acquired and reconstructed, and how physiological parameters are inferred from
-
and behavioural speech features. Integrate neuroimaging, speech and clinical data using multivariate and machine-learning approaches (e.g. UMAP). Investigate the effects of deep brain stimulation
-
and run it efficiently on different hardware architectures. For example, Google has built TensorFlow, a framework for deep learning allowing users to run deep learning on multiple hardware architectures
-
science/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning
-
learning, granular computing and knowledge discovery, machine learning, deep learning, and specifically interpretable artificial intelligence. Many innovative contributions have been achieved in theory
-
), e. a cumulative IF above 30, 2) experience and skills in the processing of medical signals and images, multimodal data, implementation of deep learning methods, data science, 3) programming skills and
-
subgroups Support public health policy, prevention and hospital planning Provide meaningful feedback to patients, clinicians and policymakers The PhD will work at the interface of machine learning, deep