Postdoctoral Fellowship (13) at Chalmers University of Technology, Gothenburg, Sweden

Are you excited about pioneering experimental quantum computing? Do you want to be part of a world-class research environment developing the next generation of superconducting quantum processors? We are seeking a research specialist to join our ambitious team at the Wallenberg Centre for Quantum Technology (WACQT).

This is an exciting opportunity for a postdoctoral position at the Quantum Technology Division of the Microtechnology and Nanoscience Department focused on innovative research in quantum technology.

Model-based control is arguably the prime framework to perform certifiably-safe regulation of dynamical systems. However, when dynamics are complex, nonlinear and partially unknown, such a model is typically obtained from observations by performing system identification — one notable example is given by Gaussian process regression to represent unknown dynamics for model predictive control. Despite the practical success, there are still many theoretical open questions regarding scalability, uncertainty bounds and deriving reliable safety guarantees. This project will aim at filling these gaps.

In this project, you will become part of a team of researchers working on fundamental aspects of data-driven decision-making inspired by, and applied to, problems in healthcare. Currently, we are investigating transferable and interpretable models for tabular data, efficient learning paradigms for medical imaging, and causally grounded and identifiable representation learning. You will have great freedom to influence your research agenda within these and related themes. Our group emphasizes methods strongly supported by theory.

The project, “Theoretical Model Organisms of Misalignment” – funded by a grant from OpenAI’s Alignment Team, awarded by the UK AI Security Institute, through the Alignment Project – aims to transform AI alignment from reactive trial-and-error into a predictive science. Instead of viewing alignment as a static property, we treat it as path-dependent: a product of the learning dynamics emerging from data, architecture, and algorithms. We use tools from statistical physics and high-dimensional probability to build tractable “model organisms” that capture the root causes of misalignment, allowing us to derive quantitative laws for when and why harmful capabilities arise.

The transition toward sustainable, resilient, and data-driven food systems requires new infrastructures that integrate microbial biodiversity, advanced genomics, fermentation science, and digital innovation. MIND, Microbial & Data-Driven Food Innovation Hub, is being established as a national competence platform. The platform is hosted at Chalmers, in close collaboration with RISE and multiple industry partners, to develop and utilize well-characterized microbial and digital resources for next-generation food and circular bio-based applications.

In this postdoctoral position, you will contribute to building MIND’s biological and digital core by integrating microbial genomics, functional characterization, fermentation science, and predictive data analysis. The project offers a unique opportunity to work at the interface of academia and industry, developing knowledge and tools that translate microbial biodiversity into safe, competitive, and sustainable food innovations.  

This project targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification. This involves formulation, implementation, and validation of novel hybrid models. The study emphasizes methodological innovation, scalable algorithms, and translation to industrially relevant multiphase reactors and separation processes, with strong expectations of high-impact publications and intellectual leadership.

Artificial Intelligence is increasingly embedded in writing for publication. While Generative AI tools may support efficiency and access, they also raise critical questions about authorship, agency, collaboration, and the diversity of scientific discourse.

This project examines:

• How AI affects self-regulation, co-regulation, and socially shared regulation during writing for publication.
• How AI-assisted writing may shape rhetorical conventions, linguistic diversity, and homogenization in published research.

The project combines micro-ethnographic, multimodal analyses of writing processes (e.g. keystroke logging, screen capture, stimulated-recall interviews) with corpus-based and genre-oriented analyses of published academic texts.

The ERC project Far-infrared (FIR) semiconductor electronics aims to establish frontline semiconductor terahertz electronics for far-infrared space instruments. The goal is to achieve high receiver sensitivity and stability with significantly lower power consumption than is currently considered state-of-the-art in the 2-5 THz frequency range. The long-term goal is to enable future atmospheric science missions that require FIR/THz heterodyne receiver solutions.

The Biomass satellite carrying the ground-breaking P-band synthetic aperture radar (SAR) was launched on 29 April 2025 and recently finished its commissioning phase. This 5-year ESA Earth Explorer mission will produce much-needed global forest biomass data using its polarimetric and tomographic radar capabilities. Our team is responsible for the algorithms which derive the biomass data product.

The post-doc project is about extending the biomass algorithm to also include data-driven, machine learning approaches. The biomass data product will be validated by data from an international network of ground-truth forest sites (GEO-TREES, geo-trees.org). The developed algorithms thus extend and complement the current generation of the biomass data product. A large-scale training data set will be generated for the biomass algorithm. The training data will be based on other remote sensing data which estimate forest biomass, e.g. from airborne and spaceborne laser scanners, since no in-situ based biomass data with global coverage exist.

The project focuses on the development and synthesis of new π-conjugated organic semiconductors for high-performance organic photodetectors. The work integrates molecular and materials design with organic synthesis, purification, and thin-film characterization, in close collaboration with partners responsible for device fabrication and testing. The overall aim is to establish clear structure–property–performance relationships and thereby enable the development of efficient and stable photodetectors for next-generation sensing and imaging applications.

The postdocs will conduct research in one or more topics of focus of the evolved star group: 1) the extended atmospheres, 2) the wind-ISM interaction regions (including hydrodynamical modeling), 3) circumstellar magnetic fields, 4) circumstellar chemistry, 5) dust (formation), 6) (binary) post-AGB stars and/or 7) the mass-loss process. In consultation with the group leaders, the research can be tailored to the successful candidate’s skill and interest. In our studies, we combine novel observational data with advanced analysis and modelling technique.