09 Postdoctoral Fellowship at Stockholm University, Sweden

The Postdoctoral fellow will work within the VR-funded project ”Tephrochronology of Sweden – tying the bag together”. The aim of the project is to get a better understanding of rapid climate changes during the Late Quaternary and to compare climate signals preserved in different high-resolution climate archives. Time-synchronous tephra markers from volcanic eruptions will be used to line up palaeoclimate records to test hypotheses regarding synchronous or non-synchronous responses to climate change and to investigate changes in the marine reservoir effect over time.

The research project is entitled, “Breaking the Inequality-Crime Cycle: Biases in Police Decisions, ‘What Works’ in Prison, and Firm Demand for Workers with Criminal Records”. The project is funded by the European Research Council with Prof. Randi Hjalmarsson (Gothenburg University) as the PI and Prof. Matthew Lindquist as the local research coordinataor at SOFI, Stockholm University. Economic inequality and segregation imply vastly unequal opportunities in many dimensions – the labor market, healthcare, informal networks, exposure to police – that can impact the chances of ending up in trouble with the law. Once caught in the justice system, it can be hard to break free, especially if a criminal record increases the barriers to employment. This cycle of crime and economic inequality can even be perpetuated across generations, if the impacts of a criminal record spill-over to an offender’s family members.  This three-part agenda considers multiple points at which the cycle can potentially be broken, including (i) police decision making and the role of training, (ii) prison reforms and prison care, including healthcare, and (iii) the role of employment opportunities – or lack thereof – for individuals with criminal records. This final part will emphasize both the potential role of firm hiring decisions and offender informal networks. The core of this economics of crime agenda is thus at the intersection of literatures within labor economics, health economics, and networks and peer effects.

The position will be associated with a grant funded by the Verg Foundation. We invite applicants working in algebraic topology. We are particularly interested in candidates whose research connects to the interests of the research group, as described above.

The position will be associated with the project Black Holes from Core-Collapse Supernovae. This project aims at the development and execution of state-of-the-art (and next generation) computational tools for simulating core-collapse supernovae, with a particular focus on black hole formation and the multi-messenger signals produced from these extreme environments.

The postdoctoral position is part of the research programme “Sustainable synergies” (www.su.se/english/research/research-projects/sustainable-synergies). The aim of the programme is to study how social policy and the welfare state can be developed to support a sustainable working life for everyone, based on both internationally comparative research and in-depth analyses of Sweden and the Nordic countries. In focus are the transitions that individuals make during their working life (for instance between work and parenting leave, or between studies and work), and the programme examines how the interplay between different social policies can support successful transitions for different groups in the labour market.

The position will be associated with the laboratory side of protein design. This includes evaluating the characteristics of designed proteins and other biomolecules that interact with proteins. These proteins can e.g. be DNA binding, bind to other proteins or have catalytic capabilities. There will be a strong focus on binding and applying laboratory techniques for evaluating interactions with proteins using techniques such as surface plasmon resonance (SPR). Theoretical knowledge of protein biophysics and modern computational methods is necessary.

The position will be associated with the projects: “Carbon Loss In Plants, Soils and Oceans” (CALIPSO, https://exeterdesignstudio.co.uk/calipso/) and “Soil microbial responses to land use and climatic changes in the light of evolution” (SMILE, www.su.se/english/news/stefano-manzoni-receives-erc-consolidator-grant-1.531350). Both projects investigate how soil processes and biota drive the cycles of carbon and nutrients, aiming to develop and test models based on optimality theories. Collaborations with several partners across Europe are envisioned, providing a unique opportunity for networking.

The project “Sector Coupling and Operations Research for Environmental Sustainability (SCORES): Emission, Price, and Welfare Impacts of the Nordic Energy Transition” will tackle Sweden’s sustainable-energy transition, which foresees power-sector decarbonization as well as electrification of industrial and heating demands, i.e., sector coupling. New power capacity will rely on variable renewable energy (VRE), e.g., wind and solar. Since VRE output is intermittent and location specific, it necessitates flexible production, e.g., hydropower. Yet, flexible plants may enjoy greater leverage under high VRE penetration to set electricity prices, which requires proactive regulation and transmission design to mitigate welfare losses from producers’ market power. Via game-theoretic models, we will identify how flexible plants’ market power is affected by sector coupling, e.g., by trading in both power and heat markets, and devise countervailing mechanisms to attenuate economic and environmental distortions. Our insights will support policymakers in crafting a welfare-enhancing transition to a sustainable power system.

The successful candidate will primarily work on two projects. The first project explores dynamic behavior of quantum matter, quantum information systems (Q bits, sensors), quantum light matter interactions and entangled and hidden quantum orders. The second project explores computational modeling and materials informatics search for novel quantum states in materials using ab initio and machine learning techniques.