-
, as well as in designing coordination strategies. Our recent work on ML-based co-optimization demonstrates some of our key research directions relevant for this position. The role holder will work
-
protein design and evolution, using molecular biology and biophysics along with the latest AI or machine learning tools. According to the development of the project there may be the chance to learn other
-
supporting aspiring startups, engaging various stakeholders of the innovation ecosystem, designing and developing knowledge & content that delivers real impact? And do you want to contribute to our mission
-
will be located in Central Cambridge Cambridgeshire, UK. The key responsibilities and duties are: Designing and developing an appropriate research methodology to address the research objective Designing
-
Partnership (KTP) project: "AI educational experiences through immersive technologies to improve corporate education". This is an exciting opportunity at the cutting edge of innovation in immersive learning
-
students but with a particular focus on incoming students. Your responsibilities will include assisting the Senior Tutorial Programme Coordinator with the design and delivery of a group tutorial programme
-
Fixed-term: The funds for this post are available for 24 months in the first instance. Applications are invited for a postdoctoral Research Associate in Energy Innovation, Economics and Policy
-
. To provide 2nd and 3rd line support activities, providing support to end users for complex technical problems. To design and develop quality technical solutions in response to business requirements. To review
-
, the Trust entered into an agreement to transfer the holdings of its historical archive to the Cambridge University Library. The PDRA will plan and execute a research project linked to the fields of interest
-
), Familiarity with other appropriate packages e.g. GraphPad, SPSS, MLWin, nQuery, PASS, XML and a working knowledge of the principles of clinical trials, understanding of the principles of clinical trial design