Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
even in the sign of change. Moreover, some hydrological reconstructions from past warm climates suggest that dry regions may have been wetter and there are plausible physical explanations
-
consortium comes in. PRELIFE unites experts across a wide range of disciplines from astronomy, biology, chemistry, computer science, earth and planetary sciences, education, mathematics, to physics. Together
-
PhD position - Modelling the emergence of information transfer in prebiotic self-replicating systems
consortium comes in. PRELIFE unites experts across a wide range of disciplines from astronomy, biology, chemistry, computer science, earth and planetary sciences, education, mathematics, to physics. Together
-
understanding of the decision-making process of street-level workers. This PhD project ‘ICONIC’ (‘International Comparative research Of street-level decisions in superdiverse Neighbourhoods In Context’) funded by
-
. This demands renewed understanding of the decision-making process of street-level workers. This PhD project ‘ICONIC’ (‘International Comparative research Of street-level decisions in superdiverse Neighbourhoods
-
organizing on both the political (and academic agenda). From a theoretical perspective, this transnational phenomenon has driven a resurgence in thinking about the legitimacy and transformational potential
-
arrangements are expected to meet. Lastly, this project must integrate theoretically and empirically driven research with normative considerations. Specifically, the project will examine where normative ideals
-
professionalising all involved partners (i.e. students, teachers, workplace assessors). Your duties and responsibilities: Develop theoretical and conceptual insights in innovative (formative, programmatic) assessment
-
research in TRL forward. Theoretical knowledge of, or experience with, machine learning such as representation and generative learning, and natural language processing. Programming skills, e.g. Python, Java
-
, creativity, rigor, ownership, and excitement to push research in TRL forward. Theoretical knowledge of, or experience with, machine learning such as representation and generative learning, data management, and