Sort by
Refine Your Search
-
environment for PhD candidates, with multiple seminars, working groups, colloquia, and a doctoral school, which also gives access to multiple training opportunities, including courses on general research skills
-
security and privacy, programming languages, formal methods, information flow, or networks and systems. Applicants can have a background from computer science, computer engineering, mathematics, or a similar
-
and hybrid stormwater solutions. Assess resilience and co-benefits of smart, sustainable stormwater strategies. Collaborate with stakeholders and utilities in Norway to apply methods in real-world case
-
discipline that combines multiple data sources along the patient pathways and develops real-world evidence to inform future clinical practice. The project will involve developing novel methods such as
-
, and communicate with external stakeholders. You are motivated to fully engage in research that is formally owned by others. Practical experience in the development of biochemical methods, cell culture
-
understanding of transport systems spanning microscopic, mesoscopic and macroscopic levels. Of particular focus will be automated planning methods that leverage emerging data and novel metrics that embed ethics
-
qualifications The Researcher will have the opportunity to join a cross-disciplinary, international team working on developing virtual testing and digitalization methods for wind turbine structures and components
-
University Hospital, Duke Regional Hospital, Duke Raleigh Hospital, Duke Health Integrated Practice, Duke Primary Care, Duke Home Care and Hospice, Duke Health and Wellness, and multiple affiliations. Be You
-
integrating local flexibility markets through distributed AI-based coordination, market mechanism design, and cloud-to-edge computing. It aims to develop scalable machine learning methods for coordinating grid
-
integrating local flexibility markets through distributed AI-based coordination, market mechanism design, and cloud-to-edge computing. It aims to develop scalable machine learning methods for coordinating grid