Postdoctoral Fellowship (23) at Chalmers University of Technology, Gothenburg, Sweden

This two-year project will develop an electrochemical exfoliation approach to produce high-quality, large-area molybdenum disulfide (MoS₂), a key two-dimensional (2D) material. The goal is to tailor MoS₂ nanosheets with controlled thickness, structure, and surface properties, and integrate them into advanced sensing applications such as sub-ppb level NO₂ detection.

Quantum networks are poised to surpass classical networks in terms of computing power and security. As a critical component of classical networks, the flexible optical fiber communication network must adapt to facilitate the transition to new communication and computing paradigms by connecting quantum processors in a networked configuration. This project focuses on deploying and enhancing quantum networks, with particular attention to switching techniques designed for quantum applications.

We seek a highly motivated and talented postdoctoral researcher to contribute to developing a state-of-the-art quantum network testbed to enable scalable quantum information exchange among quantum nodes through optical networking techniques. The successful candidate will focus on creating and managing a network environment that ensures end-to-end connectivity and resource allocation while maintaining quantum coherence.

Sulfuric acid is one of the most widely used chemicals globally, with annual production exceeding 200 million tonnes. It plays a critical role in industries such as mining, fertilizers, paper and pulp, and chemical manufacturing. A significant portion of this acid is produced as a byproduct of zinc and copper smelting, where it often becomes contaminated with mercury. This mercury is subsequently transferred into products and waste streams, contributing to elevated environmental mercury levels and increased human exposure.

It is estimated that around 300 tonnes of mercury are released annually through these processes, making them one of the top three sources of anthropogenic mercury emissions worldwide. Currently, no commercial method exists to remove mercury from sulfuric acid produced at smelters. As a result, all mercury present in the acid is carried into downstream processes and ultimately into the environment. Mercury is a highly toxic heavy metal that poses serious risks to all living organisms. Globally, around 19 million people are exposed to hazardous mercury levels through food and water. In Sweden, mercury-contaminated waters remain a significant concern, with a majority of our lakes and coastal areas exhibiting levels that threaten both human health and ecosystems.

We have recently demonstrated an electrochemical method capable of removing more than 98% of mercury from real concentrated sulfuric acid sourced from a smelting plant in small-scale laboratory tests. The project will extend and enable this method for industrial application. You will develop materials and processes enabling this technique to be scaled up and implemented globally. The research includes optimization of the electrochemical processes for mercury removal as well as safe and efficient retrieval of the removed mercury and regeneration of electrodes to create a circular process.

We are seeking a postdoc to co-design efficient and realistic simulation algorithms for noisy quantum circuits in superconducting hardware, combining quantum modeling with hardware-aware performance engineering. The work involves simulations for quantum error correction and mid-circuit operations, and will require both low-level optimization skills (e.g., SIMD, GPU, FPGA) and an understanding of quantum simulation trade-offs. The role supports integration with a 25-qubit chip and involves close collaboration with physicists and engineers to push the boundaries of scalable, high-fidelity quantum simulations.

Organic solar cells (OSCs) are a promising third-generation photovoltaic technology, but their long-term thermal stability remains a key barrier to commercialization. State-of-the-art non-fullerene acceptors (NFAs) have pushed efficiencies above 19%, yet achieving stable, glassy morphologies in multi-component NFA mixtures is an unsolved challenge – one that the vast combinatorial design space makes impossible to tackle by experiment or all-atom simulation alone.

As part of this project, you will develop coarse-grained molecular dynamics models to bridge this gap. Your work will focus on two core challenges in coarse-graining – representability and transferability — and will scale from neat NFAs to binary and many-component mixtures. The resulting models will be used to identify how molecular features and mixture composition govern glass-forming ability, generating concrete design rules for thermally stable OSC morphologies.

This position offers the opportunity to engage in advanced research on the electrochemical conversion of CO₂. You will work in a dynamic academic environment at the interface of chemistry and materials science, contributing to the green transition through carbon utilization technologies. The 2025 Nobel Prize in Chemistry, awarded for advances in metal–organic frameworks (MOFs), has underscored the importance of reticular chemistry, the design and assembly of molecular building blocks into extended, crystalline networks with tailored functionality. As fully organic counterparts, covalent organic frameworks (COFs) are constructed from light elements (e.g., C, H, O, N, B) through robust covalent linkages. These materials exhibit exceptional chemical stability, low density, and tunable electronic properties. This postdoctoral position focuses on the development of high-performance COF-based catalysts for CO₂ electroreduction. 

This project develops a real-time decision-support platform for emergency response. By integrating live sensor data from drones, wearable sensors, and thermal imaging with 3D digital twins of buildings, the system generates a shared operational picture and provides prioritized, explainable recommendations to incident commanders.

The goal for project “Digital decision support for mass casualty incidents (MCI) and military triage” funded by Swedish Armed Forces is to develop a robust and easy-to-use system for personnel with no or short medical training, e.g. combat medics. The system should dynamically estimate chance of survival and provide recommendations for which patients should be prioritized in MCI, e.g. for transport and surgery. We will use AI-based methods building on our On Scene Injury Severity Prediction (OSISP) models, and an in-house virtual test development and test environment called Virtuality to estimate effects on improving logistics and reduce mortality. 

We are seeking a highly motivated researcher who can contribute to advancing this concept. Importantly, the candidate does not need to be an expert in every aspect of the project. We are looking for someone with strong competence in at least one of the following areas and an interest in working in a cross-disciplinary environment:

• Micro- and nanofabrication specialist – expertise in clean-room processing, lithography, etching, microfluidic integration, and planar waveguide fabrication.
• Optics and photonics specialist – experience with laser systems, polarization control, fiber coupling, spectral imaging, evanescent-field optics, or advanced microscopy.
• Data analysis and computational imaging specialist – experience in quantitative image analysis, scattering modeling, signal processing, machine learning, or neural-network-based data interpretation.

The project is closely connected to national and international collaborations in advanced therapy medicinal products (ATMPs), RNA therapeutics, and nanoparticle-based drug delivery, with partners in academia, healthcare, and industry.

If you have strong competence in one of the above domains and are enthusiastic about contributing to an ambitious, interdisciplinary effort aimed at improving nanoparticle characterization for next-generation medicines, we encourage you to apply.

The long-term goal of the group is to develop rare-earth-doped photonic integrated circuit technology that can enable compact, high-performance, and scalable laser systems meeting the requirements for applications in atomic clocks, optical metrology and sensing, data communication, and quantum technologies. To this end, we will develop photonic integration of high optical quality rare-earth materials, nanofabrication, device-level design and characterization, and study light-matter integration in such nanophotonic systems. As a postdoctoral researcher, you will contribute to these research areas and collaborate closely with motivated PhD students to advance next-generation integrated photonic systems. 

The research project is about bcc-structured refractory alloys: it explores how to design bcc-structured refractory alloys that are both strong and workable for extreme conditions. By applying heterogeneity engineering, the project introduces controlled variations in structure and composition within the material. This approach enhances ductility at room temperature while preserving high strength at elevated temperatures.

About 900 million people—one-third in Africa—live in extreme poverty. Scholars are currently unable to determine to what extent poverty traps exist and whether development interventions release communities from such traps, because geo-temporal poverty data are scarce.

The Observatory of Poverty project, funded by the Swedish Research Council, addresses this challenge with two main goals:

1. Develop new methods to produce geo-temporal poverty data by training deep learning algorithms to estimate poverty from satellite images of African communities, quarterly, from 1984 to 2025; and contribute to creating the ObservatoryOfPoverty statistical package that enables scholars to produce poverty estimates for policy evaluation.
2. Use these estimates, combined with causal inference methods, to evaluate how competing aid and development interventions alter communities’ prospects of escaping deprivation.

Artificial intelligence (AI), and particularly the rapid advancement of generative AI technologies, is transforming higher education and reshaping how knowledge is created, shared, and assessed. These developments offer opportunities for pedagogical innovation, but they also raise fundamental questions about the relation between technology, learning and human agency.

The proposed project should contribute to the growing field of Artificial Intelligence in Higher Education by examining the pedagogical, cognitive, social, or institutional implications of AI adoption in higher education. We particularly welcome projects that combine empirical rigor with a reflective or critical orientation. That is, projects that do not only explore what AI can do for learning, but also what its broader consequences are for learners, teachers, and institutions.

The proposed postdoctoral research may, for example, explore:

• How students and teachers in STEM disciplines use and make sense of AI-based tools for learning, problem-solving, and assessment.
• How AI technologies influence cognitive and metacognitive processes, such as reasoning, conceptual understanding, and self-regulation in learning.
• Institutional responses, governance models, and ethical frameworks shaping the integration of AI in higher education.

(18) Postdoctoral Fellowship Position

Postdoc Fellowship Position summary/title: Postdoc in Cold clouds – bridging the gap between satellite observations and models

Deadline : May 17, 2026

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Our team is searching for a curious and productive postdoctoral fellow, interested in doing research on bio-polymer based electrolytes. In particular, while the composition of the bio-polymer is already defined, the liquid phase is aqueous and contains of different salts based on mono- or multi-valent cations. The project is in a phase where the use of vibrational spectroscopy, applied during variable conditions (like temperature and degree of hydration) is crucial to further develop the material concept.

Industrial biotechnology generates large multi-omics datasets and relies on computational models of metabolism to guide the engineering of microbial cell factories. Extracting actionable insight from these data and models requires reasoning across multiple layers of biological organization and synthesizing findings with prior knowledge from the scientific literature, a process that today depends heavily on manual expert interpretation. Recent advances in large language models, agentic systems, and retrieval-augmented generation open fundamentally new possibilities for automating and scaling this process.

In this project, you will develop intelligent computational systems that combine constraint-based genome-scale modeling and resource allocation modeling with agentic methods. Your work will span three connected directions:

• extending and applying mechanistic models of metabolism to interpret experimental data,
• building agentic pipelines that autonomously analyze omics data,
• developing systems that mine the scientific literature to ground hypotheses in established knowledge.

The position is focused on development of robust AM and post-AM processing of non-weldable Ni-base superalloys and Ti-alloys using recent advances in laser processing and in-situ process control. Position is connected to the competence centre Centre for Additive Manufacture – Metal that is focused on material and process development for powder-based metal AM.

Your major responsibility is to perform your own research as part of the CAM2 and spinoff projects within topic of PBF-LB processing of Ni-base superalloys and Ti-alloys. The position involves coordinating specific research projects in the frame of AM, collaborating within academia, industry and with society at large in the frame of CAM2 .