08 Postdoctoral Fellowship at Utrecht University, Netherlands

The positions are divided in three different sub-projects: one on colonial Suriname, one on colonial Indonesia, and one on colonial South Africa. Within each sub-project, we will study how in the period c. 1750-1950 violence and other forms of coercion played a role in household relations in relation to care and other work activities. As a team, we will investigate how work within households was divided along gender and racial lines, and look at overt and more subtle forms of coercion in this analysis.

You will do intensive archival and literature study on either of the three cases, in Dutch and preferably also former colonial archives. Therefore, the knowledge of Sranantongo, Bahasa Indonesia, Zulu, and/or Xhosa will be of high relevance for the project. Based on the research, you will write research articles. You will also be involved in organising a joint conference and in contributing to an online textbook, intended for education. As the project also has an extensive societal relevance component, you are welcome to join in on activities for that as much as you please.

In this position, you will examine recent and ongoing discussions among Iranian scholars, writers, and activists about the desirability and meaning of future political secularism in Iran. Your research contributes to understanding how ideals of political secularism are publicly justified and contested within authoritarian contexts. You will be expected to:

• engage theoretically with academic debates on political secularism, in particular normative political philosophy on deliberative democracy and political theology;
• map and conduct a discourse analysis of transnational Iranian public political debates on political secularism on television, radio, and social media channels, as well as books, manifestos, charters, and opinion pieces;
• evaluate the deliberative quality of public arguments made for political secularism in Iran, using both critical and constructive argumentation.

As an postdoctoral researcher, you will actively collaborate with the other researchers of the Vidi project based at Utrecht University, present at international academic conferences, contribute to peer-reviewed publications, and share research results through academic and public engagement activities. .

The Department of Methodology & Statistics invites applications for a fixed-term research position in statistics and data science, as part of the NWO (Dutch Research Council) project “Digital Behavioural Data to Enhance the Quality of Regional Estimates of Attitudes towards LGBTQ+ People”, led by Dr Angelo Moretti and Dr Camilla Salvatore.

This project aims to shed light on the use of big data, in particular data obtained via OpenStreetMap and Google Trends, as auxiliary information to enhance small area estimates of attitudes towards LGBTQ+ communities in Europe. We consider the following research questions (RQ): RQ1: which variables obtained from big data may help to provide better quality estimates compared to variables obtained from traditional data sources? RQ2: how can we combine the two different data sources in area-level models to measure attitudes towards LGBTQ+ people? RQ3: when both traditional and non-traditional data sources are used as auxiliary information, do we need to apply statistical approaches to correct for selectivity and coverage errors?

In the position you are responsible for:

1. Looking for potential data sources and identify their advantages and disadvantages within sub-national analyses
2. Identifying measurements errors and selectivity issues arising from those data sources and formulate modelling strategies on how to take into account for those
3. Estimate small area models and produce maps after assessing modelling results
4. Produce Open Source R software
5. Writing reports and research articles where you will be co-author.

In recent years, universities and museums across Europe have become more attuned to their colonial entanglements and developed innovative approaches to reflect and deal with their colonial heritage, including the renaming of buildings and the decolonisation of curricula. Such steps have often, though not exclusively, been taken in collaboration with partners from former colonised countries. COLUMN brings together partners from the creative industries, seven European universities from the Coimbra network, and partner universities from across the globe to explore further how we can effectively decolonise university heritage. Through analyses of anthropological collections, botanic gardens, university campuses, and intangible heritage practices, COLUMN aims to develop policy recommendations and good practices for decolonial and inclusive collaborations around European university heritage.

As a postdoctoral researcher you will join a team of scholars from the Netherlands, Italy, South Africa and Suriname. You will contribute to two co-curated heritage interventions:

1. Building on completed ethnographic and historical research, the first intervention will be an exhibit that presents the complex colonial history of physical anthropology (plaster casts) in the Dutch Indies. This will be in collaboration with other scholars (Prague, Pretoria and Bologna), an exhibition design studio, curators from the University Museum Utrecht, and funded curators from Indonesia. The aim is to develop a presentation that is both academically grounded and accessible to a broad public, inviting critical reflection and dialogue.
2. In collaboration with another UU postdoctoral researcher, the Botanic Garden of Utrecht, a Surinamese artist-in-residence, an exhibition design studio and Surinamese colleagues, a heritage education intervention will be developed. Just as in the first intervention, the historical research will have been completed. This intervention will also include educational programming aimed at public engagement.

This position is embedded in a multi-institutional EU-funded project Intergenerational Open Geospatial Carbon Registry 

 (OGCR). The project is aimed at quantifying and mapping carbon storage potential and future carbon removal and carbon farming policy scenarios, integrating process-based knowledge with machine learning.

You will work closely with the Utrecht University team and OpenGeoHub together with other project partners, to develop and implement surrogate and hybrid modelling frameworks, produce pan-European maps of carbon storage potential, and contribute to interactive visualisation and dissemination of results tailored to stakeholder needs.

Your key responsibilities include:

• simulating carbon reduction potential at parcel level under climate change scenarios across Europe;
• design and implement geospatial hybrid and mechanistic modelling frameworks using process-informed machine learning and data augmentation for soil and biomass carbon forecasts;
• developing a computational framework for data production in cooperation with Research Software Engineers;
• collaborating and coordinating with partners across Europe: data sharing, code exchange, joint publications and reporting.

This position is embedded in a multi-institutional EU-funded project Intergenerational Open Geospatial Carbon Registry 

 (OGCR). The project is aimed at quantifying and mapping carbon storage potential and future carbon removal and carbon farming policy scenarios. It integrates process-based knowledge with machine learning and especially aims to spatially quantify uncertainties.

You will work closely with the Utrecht University team and OpenGeoHub together with other project partners, to develop surrogate and hybrid modelling frameworks combining process-based models with data science methods. A second focus will be to establish methods for geospatial uncertainty quantification of such models for aggregated units to support pan-European mapping of carbon storage potential and contribute to interactive uncertainty visualisations and dissemination of results tailored to stakeholder needs.

In this project, we aim to apply deep learning techniques to predict precipitation fields over the Greenland Ice Sheet and the Himalaya in one combined project. For the Greenland Ice Sheet, the focus is on the spatial precipitation distribution on monthly and yearly timescales, for various climate states and ice sheet geometries. As training data, precipitation fields from the regional climate model RACMO will be used, which is a leading model in this field and developed and run within IMAU Ice and Climate research group. The created emulator can be used, for example, to rapidly provide precipitation fields for long-term (millennia) ice sheet simulations with changing geometry. For the Himalaya, the focus is on instantaneous, high-resolution precipitation fields for a static topography. Here, model data from the regional climate model WRF, operated the Mountain Hydrology research group, will be used for training. These data are available at different spatial resolutions, and the aim of the emulator is to downscale precipitation fields to an unprecedented resolution that better resolves the complex Himalayan topography.

Despite the enormous attention machine learning techniques receive nowadays, robustly emulating precipitation fields is an outstanding challenge. In this project, the successful candidate is expected to apply and expand existing knowledge and practices of machine learning techniques to create these novel emulators.

The position is split-up in two interrelated projects. For the first project focussing on Greenland, you will choose, develop and train the machine learning technique to emulate precipitation fields over the ice sheet. Next to that, you will run the regional climate model over Greenland using various ice sheet geometries to create a training dataset that sufficiently constrains the emulator. The results are expected to be published in peer-reviewed literature, and the emulator will be made publicly available to the international research community.

In the second project, a similar path will be taken, except that existing WRF simulations will be used and applied to the Himalaya with a fixed geometry.