Postdoctoral Fellowship (09) at Stockholm University, Sweden

The postdoctoral position is attached to the institute’s Social Policy research unit. Members of the unit study the causes and consequences of social policies at both individual and societal levels. Research is conducted using macro- as well as micro-level approaches, primarily with quantitative data. Macro analyses examine social policy development within and across countries, while micro analyses focus on the impact of social policies on individuals’ living conditions and life courses. The institute hosts the leading source of comparative longitudinal social policy data: the Social Policy Indicators Database (SPIN). In addition, the unit’s researchers use a wide range of data such as administrative registers and international surveys.

After a period of hyperglobalization, protectionist policies have become more entrenched over the past decade. States have produced a slew of domestic subsidies, investment regulations, and labor migration policies––policies that can benefit certain industries, but are likely to curtail growth and multilateral economic cooperation. Why some citizens support protectionism that is costly to them, while others do not, is the central puzzle that the ERC-funded project “PROTECT – Protectionism in Place: Explaining How Subnational Change Affects Public Support for Protectionism” will examine. It will focus on the effects of changing local circumstances on public attitudes towards protectionism. For this purpose, the project will collect new survey and subnational context data from select western and non-western countries. 

The postdoctoral fellow will conduct research within the ERC-project “PROTECT – Protectionism in Place: Explaining How Subnational Change Affects Public Support for Protectionism”. As a postdoctoral researcher within the project, you will primarily work with the collection and analysis of new subnational data from official statistics, meteorological re-analysis, and satellite data, and be responsible for analyzing and mapping the data. You will analyze the data in relation to survey data to examine how changing local circumstances affect public attitudes towards protectionism. This involves methods such as machine learning and instrumental variables estimation, as well as an application of a machine learning–based extension of the standard difference-in-differences (DID) approach to evaluate policy in relation to public opinion. Hence, fluency in machine learning methods, programming in Python, as well as knowledge of neural nets are important merits. You will be part of a collaborative research group engaged in quantitative research on the effects of changing local circumstances on public support for protectionism. Additionally, the postdoctoral fellow is expected to participate in the Higher Seminar in International Relations at the Department of Economic History and International Relations. 

We seek two highly motivated postdoctoral researchers to develop new mathematical and computational methods for modeling developmental and evolutionary dynamics. The positions are based in Assistant Professor Lisandro Milocco’s research group at Stockholm University and SciLifeLab, within Sweden’s national Data-Driven Life Science (DDLS) program funded by the Knut and Alice Wallenberg Foundation.

There are two possible research directions. Depending on expertise and interest, the successful candidate may focus on one or combine aspects of both.

The first direction concerns the data-driven discovery of dynamical rules underlying developmental trajectories. The aim is to develop and analyze quantitative frameworks that learn dynamical structure directly from time-series data. This includes methodological work on nonlinear state-space reconstruction, system identification, reservoir computing and related recurrent architectures, with emphasis on robustness, generalization, and performance in high-dimensional and noisy biological datasets. See this publication for additional details: https://doi.org/10.1111/ede.12449.

The second direction explores how developmental processes shape and constrain phenotypic variation. Although phenotypic datasets are often high-dimensional, variation frequently lies along a much smaller number of effective directions. This suggests that evolution operates within a restricted space of developmentally possible forms. This project aims to identify such low-dimensional structure from phenotypic data and incorporate it into mathematical models of trait evolution across phylogenies. The work combines dimensionality reduction and geometric data analysis with the development of statistically rigorous comparative methods for continuous trait evolution.

Nordita, the Nordic Institute for Theoretical Physics, invites applications for the Nordita Senior Fellows Program, a prestigious senior postdoctoral fellowship designed for researchers who are approaching academic independence. We are looking for you who are excited about advancing science through collaboration and open exploration. The listed qualifications below serve as a guide rather than a checklist. We encourage applications from candidates with complementary strengths and diverse research trajectories, including those who may not meet every requirement. In the course of this position you will be able to progress your CV towards future academic positions.

The progam is aimed at developing the next generation of research leaders in theoretical physics and related fields in the Nordic region and internationally. Nordita Fellows are expected to conduct independent, ambitious research within Nordita’s core areas of theoretical physics. In developing their independent research program, Nordita Fellows are encouraged to collaborate both with Nordita faculty mentors and with other researchers more broadly. For this call, particular consideration will be given to candidates working in:

• Quantum matter and quantum materials
• Quantum sensing and quantum technologies
• Complex systems and networks
• Soft and living matter.

The project is based on the assumption that social position can influence health, but that health can also influence social position. It aims to develop a deeper understanding of the processes that give rise to social inequalities in health through longitudinal analyses of the interaction between working life factors and health. The data material consists of the Labour Force Survey (LFS) as well as population registers on demographic composition, socioeconomic conditions, and health from Statistics Sweden (SCB), the Swedish Social Insurance Agency, and the National Board of Health and Welfare.

Complexity is a fundamental concept that quantifies the difficulty of accomplishing a particular task, given a specific set of allowed, simple operations and a procedure to achieve it. As such, it is an important part of many fields from mathematics to computer science. Surprisingly, physics still lacks a precise definition of quantum complexity and providing it is one of the most urgent problems at the cutting-edge of quantum theory and quantum gravity.

The position within the ERC Consolidator project “Quantum Complexity from Quantum Field Theories to Quantum Gravity” (PI: Pawel Caputa) will aim to fill this gap.

The goal of the project is to develop universal measures of quantum complexity for many-body systems, quantum field theories and quantum gravity. The milestones of the project include: synthesizing and developing physical measures of complexity, formulating them in exact holographic models, and applying them to shed new light on the physics behind black hole horizons and spacetime singularities.

Matrix theory is an important approach to non-perturbative string theory in which quantum gravity emerges from relatively simpler quantum systems of matrices. Such a matrix quantum mechanical systems arise from certain decoupling limits in string theory that zooms in on states protected by supersymmetry. The same decoupling limits also underlie the AdS/CFT correspondence.

The position within the Olle Engkvists Stiftelse project “Emergent Universe Matrix Quantum Mechanics” (PI: Ziqi Yan) aims to deepen our understanding of matrix theory as well as broaden the landscape of decoupling limits in string theory, using the newly developed non-Lorentzian geometric techniques. Mapping out such decoupling limits and their quantum properties will not only deepen our understanding quantum gravity in substring regimes but may also lead to holographic constructions of gravity on realistic backgrounds.  

The milestones of the project include: (1) classifications of decoupling limits zooming in on orthogonal branes; (2) generalizations of decoupling limits to giant gravitons and (spin) matrix theory and field-theoretic formulation of spin matrix theories; (3) constructions of holographic dualities with de Sitter, flat space, and non-Lorentzian bulk geometries.

The postdoctoral position within the VR project “Emergent spacetime from non-relativistic holography” (PI: Niels Obers) aims to develop a new theoretical understanding of how spacetime and gravity can emerge from quantum-mechanical degrees of freedom in non-relativistic holographic frameworks. The project focuses on establishing and using controllable limits of string theory and holography—particularly non-Lorentzian (e.g. Galilean/Newton–Cartan and closely related) geometries and dual field theories—to map out the underlying duality structure and to illuminate non-perturbative phenomena with connections to black hole physics and quantum field theory.

The Department of Ecology, Environment and Plant Sciences invites applications for a postdoctoral position within the research project “A digital twin of phytoplankton: creating a digital twin of phytoplankton to understand eco-evolutionary dynamics under global change.”

The project aims to develop a computational digital twin of phytoplankton communities by integrating trait-based eco-evolutionary theory, high-frequency in situ monitoring data, and machine learning methods. Using a unique multi-year dataset, the project will infer fitness and growth functions governing phytoplankton populations and communities under varying environmental and biotic conditions. The overarching goal is to improve mechanistic understanding and predictive capacity regarding community reorganization and adaptation under environmental change.

The project is led by Professor Jon Norberg (Stockholm University) in close collaboration with Dr. Francesco Pomati (EAWAG, Switzerland) and is funded by the Swedish Research Council (Vetenskapsrådet).