PhD Optimizing evacuation decisions under infrastructure failure

Climate change is rapidly increasing the frequency and severity of extreme weather events. The Netherlands, where 26% of the territory lies below sea level, is particularly vulnerable to flooding and other climate-related hazards. Effective a priori evacuation plans are known to reduce loss of life, yet uncertainty surrounding both the spatial development and temporal progression of disasters often renders theoretically optimal evacuation decisions unviable in practice. Designing evacuation strategies that remain efficient, feasible and resilient when critical infrastructure is threatened by large-scale disruptions therefore remains a major challenge in Operations Research.

The RESCUE project develops new multi-stage optimization models for pre-disaster evacuation decisions on critical road and shelter infrastructure subject to failures. Combining robust optimization, stochastic programming and copula theory, the project assesses the resilience of both self-evacuation and shelter-in-place strategies. For this purpose, the project draws on concepts from network flow problems and assignment problems. Models are validated using hydrodynamic simulations of Dutch flash flood scenarios.

Your duties
RESCUE (The Resilience of Evacuation Strategies on Compromised and Unreliable Evacuation infrastructure) is a project from the Vrije Universiteit Amsterdam (VU), funded by the NWO Open Competition - Domain Science. The PhD candidate will be employed at the Department of Operations Analytics and supervised by Dr. Pascal Wissink (PI), in close collaboration with Dr. Markus Leitner, Prof. David Wozabal and Prof. Bas Kolen (University of Amsterdam).

Your duties include:

• Conduct high-quality doctoral research leading to journal articles and a PhD dissertation.
• Develop optimization models, algorithms, and computational methods for robust evacuation planning.
• Present your work at scientific conferences and workshops with academic and industry partners.
• Participate in PhD training courses, with the opportunity to obtain a PhD teaching qualification certificate.
• Contribute to the teaching activities of the department (lectures, tutorials, exams, grading, etc.).
• Work closely with the supervisory team and help translate results into practical recommendations for disaster preparedness.

Tandartsdocent bij Digital Dentistry

Als docent begeleid je gemotiveerde studenten die zich voorbereiden op een carrière in de tandheelkunde. Met jouw ervaring als tandarts binnen de digitale tandheelkunde vergroot je hun vakinhoudelijke kennis en draag je bij aan de ontwikkeling van de volgende generatie tandartsen. Je fungeert als vraagbaak, inspirator en rolmodel.

Daarnaast ontwikkel je nieuwe didactische vaardigheden en werk je in een dynamische academische omgeving, samen met collega’s uit diverse disciplines. Hierdoor blijf je op de hoogte van de nieuwste ontwikkelingen binnen het vakgebied en breid je jouw professionele netwerk verder uit.

Jouw taken

• Bachelor en Master-studenten Tandheelkunde begeleiden/coachen met verschillende toepassingen van digitale tandheelkunde binnen het (pre) klinische onderwijs
• studenten ondersteunen bij het ontwikkelen van hun (pre) klinische competenties door middel van verschillende digitale tandheelkundige middelen, zoals VR-simulatie en CAD-CAM
• actief participeren in de voorbereiding, uitvoering en evaluatie van het digital dentistry
  onderwijs

Post-doc Computer-Aided Drug Design

The successful candidate will develop and apply Computer-Aided Drug Design (CADD) methodologies to advance drug discovery projects. Their work will focus on improving the molecular understanding of ligand-protein interactions and translating these insights into the design of novel small-molecule, drug-like compounds.

The candidate will join a well-integrated, multidisciplinary team and help guide ongoing drug synthesis and biological screening activities. The results are expected to lead to publications in high-quality scientific journals and to contribute to the specific goal of developing new medicines for Alzheimer’s disease.

Post-doc Design and synthesis of Enzyme Inhibitors

Using your experience in small-molecule organic synthesis, you will design and synthesise new heterocyclic molecules that are able to inhibit PDE enzymes. You will synthesise, isolate and characterize novel drug-like molecules. Your compounds will be screened for enzyme inhibition, enabling the development of structure-activity relationships that guide the synthesis of additional compounds.

The candidate will join a well-integrated, multidisciplinary team and work closely with colleagues who conduct Computer-Aided Drug Design (CADD) studies and biological screening. The results are expected to lead to publications in high-quality scientific journals, while also contributing to the specific goal of developing new medicines for Alzheimer’s disease.

PhD position in Social Science Genetics – VU Amsterdam & Amsterdam UMC

The Doctoral Network

ESSGN brings together eight Universities with a shared interest in social science genetics, i.e., in incorporating genetic information to improve our understanding of age-old questions in the social sciences, such as the origins of inequality, the ‘nature versus nurture’ debate, and the extent to which the interplay between environments and genes is important in shaping life chances. The consortium consists of an interdisciplinary group of academics, spanning demography, economics, epidemiology, genetics, political science, psychology, sociology, and statistics, as well as seven non-academic partners experienced in and committed to using data science to better understand inequalities in life chances. The partners include government bodies (GO Science UK), policy research institutes (RAND Europe, Netherlands Interdisciplinary Demographic Institute [NIDI]), charities (the Health Foundation), data infrastructure networks (CentERdata-ODISSEI) as well as economics consulting (Prometeia).

ESSGN creates, nurtures, and maintains a stimulating and world-class training environment in the interdisciplinary study of social-science genetics and geno-economics. Training will be provided through: i) courses in the biological foundations of genetic inheritance; ii) practical training in cutting-edge, multidisciplinary methods in social science genetics, including in state-of-the-art computational and bioinformatics methods for analysing big data and in statistical techniques for empirical research; and iii) cutting-edge academic research, to prepare the doctoral student to be a successful research scientist, but also for the professional job market in industry, technology and policy. We will coordinate and support skill development as well as monitor well-being and progress towards the PhD.

The PhD projects will jointly contribute to answering a key research question that has occupied social scientists for decades:

To what extent do inequalities in life chances arise from genetic variation, environmental factors, and their interplay, and what can we do about it?
The PhD project will go beyond the state-of-the-art by (i) using Europe’s largest and most comprehensive multi-generation databases to separate direct genetic effects from parental genetic and socio-economic factors that shape the rearing environment; and (ii) by exploiting the large toolbox of causal inference methods used in econometrics and statistics to estimate how environmental contexts causally protect individuals with genetic risk.

We will incorporate genetic information into the social sciences to explore how genetic and environmental characteristics jointly shape inequalities in life chances by (1) analysing to what extent genetic (‘nature’) and environmental (‘nurture’) factors contribute to inequality of opportunity and intergenerational mobility, and (2) establishing how nature and nurture jointly shape inequalities in life chances.