Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
-cell autonomous consequences of progressive accumulation of α-synuclein aggregates in cells and/or mechanisms involved in intercellular spreading of such processes. The successful candidate will develop
-
Collaboration and Augmented Decision Making We invite particularly proposals for specialized courses and courses that do not overlap with our current teaching portfolio (please check our course catalogue ) - for
-
teaching environment and to the faculty’s overall research strategy. You will contribute to the development of the department both individually and in collaboration with others via your research of high
-
provides research-based teaching of the highest quality and is responsible for a large part of the medical degree programme. Academic staff contribute to the teaching. English is the preferred language in
-
AI-driven antimicrobial resistance detection via mass spectrometry your position is primarily research-based but may also involve teaching assignments. You will contribute to the development
-
an important part of the research environment and that you will contribute positively to the social working environment. We also expect that you will take part in our teaching activities and that you will report
-
, neuroscience and personalised medicine. The Department of Biomedicine provides research-based teaching of the highest quality and is responsible for a large part of the medical degree programme. Academic staff
-
inflammation, membranes, neuroscience and personalised medicine. The Department of Biomedicine provides research-based teaching of the highest quality and is responsible for a large part of the medical degree
-
and valuation We invite proposals for specialized courses that have a minimal overlap with our current teaching portfolio. We encourage you to explicitly state how your proposal differs from
-
methods for approaches such as model-data fusion techniques. The main focus of your position will be to develop and apply a scalable process-based modeling framework to evaluate climate-smart management