PhD Position in Categorical Foundations of Type Theory

In this project, we see the aforementioned connection between mathematics and computer science as one between category theory and functional programming languages. Whereas The connection between mathematics and Martin-Löf type theory can be formalized as an equivalence between structured categories (e.g., comprehension categories, categories with families, or the like) and Martin-Löf type theories. We envision that the analogous correspondence for domain-specific variations on Martin-Löf type theory must incorporate more structured categories (e.g., enriched versions of comprehension categories or the like).

Your research will explore this area, allowing you to shape your own focus, potentially involving:

• exploring categorical semantics for these type theories using the tools of enriched category theory;
• developing a common framework for these semantics;
• formalizing aspects of this research;
• formalizing mathematics within one of these type theories;
• implementing aspects of this research.

You will be supervised mainly by Paige Randall North, with co-supervisors selected as appropriate. You will be employed jointly by the Department of Mathematics and the Department of Information and Computing Sciences.
Your responsibilities will include:

• conducting this research, both individually and as part of a team;
• participating in scientific life at Utrecht University and in the Netherlands, for example by attending and organizing seminars;
• participating in scientific life more globally, including publishing papers and attending workshops and conferences;
• contributing to teaching in mathematics and computer science, providing the opportunity to gain teaching experience.

Business Intelligence Engineer

Je werkt aan het beheer én de doorontwikkeling van onze BI-omgeving. We werken met een Enterprise Datawarehouse, gemodelleerd volgens Datavault, waarin de belangrijkste bedrijfsvoeringssystemen zijn geïntegreerd. Vanuit een aantal dimensioneel gemodelleerde datamarts worden de gegevens in een SAP BusinessObjects (4.3)-omgeving ter beschikking gesteld. Daarnaast ontwikkelen we een apart dataplatform in Azure voor o.a. analyse- en procesminingdoeleinden.

Je werkt aan oplossingen die gebruikt worden voor sturing en besluitvorming binnen de universiteit. Daarbij is er ruimte om nieuwe ideeën in te brengen en oplossingen te verbeteren.

Je houdt je bezig met:

• technisch beheer van onze omgeving;
• ontwikkelen, testen en onderhouden van ETL-processen;
• bijdragen aan de volgende generatie van onze BI-architectuur en datawarehouse oplossing;
• inventariseren en specificeren van functionele gebruikersbehoeften;
• ondersteuning van gebruikers bij datavraagstukken.

Je werkt in het team System Teams Platformen, bestaande uit acht BI-collega’s die samenwerken aan een toekomstbestendige dataomgeving voor de hele universiteit.

PhD Position in Nutritional Immunology

Low grade inflammation affects a growing population in western countries, leading to an increase in immune disorders such as autoimmunity and allergies, including asthma, which affects 10% of adults in industrialized countries. Type 2 asthma cannot be cured but it can be treated with inhaled corticosteroid (ICS) as controller medication. Some asthmatics have reduced serum levels of n-3 LCPUFA, EPA and DHA, due to low intake or reduced FADS (fatty acid desaturase) enzyme activity. Furthermore, serum vitamin A and D levels are generally low in the western population, while these vitamins can promote tolerogenic immune modulation and/or barrier protection. This project aims to highlight the relevance of these nutrients in managing pulmonary inflammation and therapy guidance, using state-of-the-art human in vitro mucosal-immune cell models.

As a PhD candidate you will further develop bronchial epithelial barrier models and study their interaction with innate and adaptive immune cells, while assessing the anti-inflammatory and barrier protective effects of n-3 LCPUFA plus vitamin A/D as adjunct treatment to corticosteroids. You will present your results at national and international conferences in the fields of Nutrition, Allergy, Pulmonary disease and Pharmacology, reflecting the broad scientific scope of the project. Working across these areas makes this position challenging and highly relevant, as you will actively bridge these scientific fields! It will also allow you to highlight the strength of combining pharmaceutical and nutritional approaches, an opportunity which is often overlooked.

Within the Division of Pharmacology our research group has long-standing experience in the field of Nutritional Immunology, with established expertise of using n-3 LCPUFA for allergy prevention in vitro and in pre-clinical models.

This project is part of the European doctoral network LipidBRIGHT, offering the opportunity to collaborate in a network of academic and non-academic partners that bridge clinical with basic research on lipids in chronic inflammatory disorders.

PhD Position in Music Computing

The core aim of the project is to design explainable computational models that drive high-impact applications in Music Information Retrieval (MIR), while advancing our computational understanding of musical style: what defines it, what are its elements, how is it structured and perceived, how does it vary?

The project will build on various theories. A promising starting point is Leonard B. Meyer’s theory of musical style, which defines style as a replication of patterning. A central challenge in this approach is to identify structural elements of music as instances of patterns. What these patterns are differs culturally and historically. The body of literature on topic theory (founded by Leonard Ratner) offers a point of departure to identify such patterns and to understand the way in which these are replicated and perceived. For example, a fragment of music could allude to a ‘fanfare’, or to a ‘horn call’, to just mention two examples out of many. These kinds of patterns have many occurrences throughout music history. Can we design computational models for such topics? How do topics function in game music and film music? How are different musical styles interconnected by occurrences of topics?

We also envision to connect with current understanding of music cognition, specifically building on insights on musical memory. There is a class of modular cognitive models of music processing that include a ‘musical lexicon’ as one of the cognitive modules. This ‘musical lexicon’ determines for a given listener what musical patterns can be recognized. Understanding of this personalized music perception plays a role in user modelling for interactive music systems.

An important challenge lies in designing models that go beyond merely achieving high accuracy in classifying musical styles or genres, or in detecting specific musical patterns. The process of modelling facilitates the understanding of the patterns through a computational lens. This calls for strong expertise in computational methods, machine learning, and data modelling combined with solid knowledge of music. We particularly aim to cover a broad range of musical traditions and cultures world-wide, both contemporary and historical.

In this project you will:

• prepare data sets representing a wide range of musical styles, including both audio and symbolic formats;
• design appropriate data structures for representation of music;
• design computational detectors for various musical patterns;
• design, implement and evaluate computational models of musical style;
• apply these models in several case studies.

Furthermore, you will communicate results in academic presentations and publications, and ultimately in a PhD thesis. During the project, you will expand your academic network. A moderate percentage of the time will be spent on teaching tasks within the department, providing you with the opportunity to gain experience in teaching.

PhD “Climate Change and Cooperation in Asia, 1945 to the Present"

Around the world, climate risks facing agriculture have intensified in recent years. The capability of rural societies to adapt to or mitigate the effects of climate change hinges on their capacity for cooperation. This project studies the historical roots of cooperation in rural societies faced with climate uncertainty. In the post WWII period, rural societies in Asian countries underwent a dramatic transformation. The introduction of new agricultural technologies increased crop productivity and rural incomes. At the same time, these changes reshaped economic, political, and social power in rural communities. They also exposed farmers to new forms of climate risk, contributing to variation in cooperation and conflict. Focusing on local processes during this period allows us to trace when and why cooperation breaks down or endures, and to assess the consequences for sustainable development.

Through a comparative and long run study of rural Asia, the project will investigate interactions between climate, institutions and cooperative behaviour. This will involve the collection of archival data at the household or village level to understand how local conditions and cooperative behaviour changes over time and across regions or countries. The project will analyse the drivers of cooperation through historical analysis, with additional insight from the field of social psychology that helps to understand cooperation at an individual and group level. By clarifying when cooperation succeeds or fails in historically climate-vulnerable agricultural contexts, the project offers lessons for climate adaptation in other countries around the world.