How does climate change really impact labour migration in the Global South? There is much scientific disagreement about the impact of climate change on human migration in the Global South. Our current forecasting methods have numerous problems, leading to inaccuracies in expected numbers of climate migrants. Using a new methodology, this research project investigates how droughts influence the availability of agricultural work across all 750,000 villages of rural Nigeria and India, and how this triggers out-migration among the male working population. Through methodological innovations this project helps us better understand the relationship between climate change and male labour migration.

Climate change impacts are emerging as key drivers of forced migration, particularly among hundreds of millions of agrarian households in the developing world. There are signs that temporary male labour migration appears to offer an alternative way out for “trapped” agrarian populations, but little is otherwise known about this escalating trend. Forecasting climate migration is crucial to prepare for its major societal impacts, but existing approaches are coarse and datasets on labour migration almost non-existent. The novelty of this project is that I address these dire data gaps, and will forecast climate migration at very fine spatial scales. India is used as a case study. I combine advanced satellite-based weather observations with a systematic tracking of agrarian workforces across 250,000 settlements using village-level microdata. I investigate spatial correlations between historical climate change and movement out of farming. This quantitative macro-analysis “from above” is combined with primary data collection on labour migration “from below”, creating a unique, custom-made dataset that can be used for fine-scale climate migration modelling. This approach can serve as a new paradigm in this field of studies, and results can inform policy-makers on the ‘when’, ‘where’, and scale of future climate migration flows under different climate scenarios.

This research includes laboratory and field analog studies. We used experimental setups to study the stability of organic molecules in different surface environments on Mars and the importance of those environments for prebiotic chemistry on Mars. We also conducted field studies in a Mars analog environment (Icelandic lava tubes) to gain insight into potential geochemical features of life in the subsurface of Mars. We conclude that bringing laboratory and field analogs together, with both the total analog environment in the field and specific parameters that can be determined in the laboratory, is a necessary step in biosignature research on Mars.

The brain is the most intelligent information processor we know, but it does not come into the world this way. Most of the brain’s intelligent functions have to be learned from experience with the world. The key to understanding the brain, therefore, is to understand how the brain learns. I will target this important question, by combining knowledge about the brain and the environment from which it learns, with insights from self-learning computer algorithms. Thanks to recent, exponential developments in these algorithms, we are now in a position to apply similar techniques to model learning in the brain. Using visual perception as a test bed, I will adapt existing supervised learning methods into a new computational model of unsupervised learning in the brain’s visual cortex. From this model, I will distil concrete, testable predictions that I will validate against data from human participants performing perceptual tasks. By thus dovetailing computational and empirical methods, this research aims to understand how neurons wire together into complex information-processing networks. This not only addresses a fundamental and outstanding question in our understanding of the brain, but may also aid the development of more advanced self learning computer algorithms based on the same principles.

Women and racial-ethnic minorities are underrepresented in (higher) leadership positions. Research on the psychological underpinnings of this underrepresentation traditionally focuses on stereotypicality bias explanations (i.e., women and minorities do not ?fit? the typical image of leaders). The present research introduces an additional, prototypicality bias explanation (i.e., women and minorities may not be perceived as ideal representatives of groups). Besides more elaborately specifying the underlying psychological mechanisms, the introduced Dual Pathway to Leadership Model also focuses on how the context (crisis and diversity policy) affects female and minority leadership through its influence on these mechanisms. Addressing shortcomings in the literature and novel research-questions, my main goal is to illuminate how biases in social-cognitive and social-identity processes simultaneously affect female and minority leadership-emergence and -evaluations under differing contexts. In order to attain this goal, I will perform four elaborate experiments which have important theoretical and practical implications for both glass ceiling researcher and work organizations. KEYWORDS: the glass ceiling, leadership, diversity