Sorry, you need to enable JavaScript to visit this website.

Department of Archaeology

 
Read more at: ArchBiMod – Agent-Based Modelling to assess the quality and bias of the archaeological record

ArchBiMod – Agent-Based Modelling to assess the quality and bias of the archaeological record

Archaeological data is often biased and incomplete. This is a well-known issue for most archaeologists. Although studies of specific sites and small regions can have this into account, the effect of this problem increases exponentially as archaeologists expand their chronological and geographic frame, and try to answer questions related to general dynamics and broad human processes.


Read more at: iMapNut: Machine Learning to Map and Address Causal Factors of Child Malnutrition in Low- and- Middle- Income Countries

iMapNut: Machine Learning to Map and Address Causal Factors of Child Malnutrition in Low- and- Middle- Income Countries

This project aims to improve the poor integration of localized data linking various WASH dimensions (infrastructure, access, practices) and children nutritional status at the population level as well as the poor involvement of policy makers concerned with WASH in local and country level nutritional programs. Our network aims include:
1.