Postdoctoral Fellowship (30) at Chalmers University of Technology, Gothenburg, Sweden

 The pace of the transition to a fossil-free transport sector will depend on many factors, such as the vehicle and infrastructure design, how it influences the haulage companies’ economy, and how the haulage company is affected by policies and legislation. Understanding and quantifying these connections and interactions will help policymakers and vehicle manufacturers make well-informed decisions toward a fossil-free transport system. This project will take a vehicle-focused approach to the study of electrification of the transport system. The aim is to develop mathematically sound tools capable of connecting vehicle characteristics to a national CO2 emission impact and to quantify these using statistical relations. 

The project focuses on the energy-efficient coordination of electric vehicle (EV) fleets for resilient transport systems during emergency evacuations. Climate change is increasing the frequency and severity of natural disasters, including floods, wildfires, and extreme weather events, making efficient large-scale evacuation planning increasingly important. At the same time, the transport sector is undergoing a rapid transition toward electrification. While EVs are central to achieving climate-neutral mobility, most existing evacuation planning methods still assume conventional fuel vehicles and do not account for EV-specific challenges, such as limited battery capacity, variable energy consumption, charging infrastructure availability, recharging requirements, and interactions with the electricity grid. This multifaceted gap presents a significant challenge for designing evacuation strategies that remain effective in future electrified transport systems. The project aims to develop novel AI methods for coordinating fleets of EVs during large-scale emergency evacuations under energy and infrastructure constraints. To address this challenge, the research will leverage reinforcement learning, deep learning, and generative AI, and evaluate against the research front in mathematical optimization strategies, to enable efficient, robust, and adaptive evacuation planning. 

DRIVE PR – Data-Driven Product Realisation is a large collaborative research initiative at Chalmers University of Technology and the Wingquist Laboratory. The project brings together leading industrial and academic partners to transform product realisation from data-enabled to truly data-driven practices.

The advertised position will mainly be connected to DRIVE PR, but may also be linked to related projects within data-driven product realisation, digital engineering, systems engineering, geometry assurance, automation, or model-based development.

The project addresses how advanced digital infrastructure can be used to reduce uncertainty, risk and variation in industrial product realisation. It connects engineering design, AI, digital twins, simulation, manufacturing systems, 5G/6G, decision support and real industrial demonstrators. The overall ambition is to demonstrate significant reductions in costs arising from uncertainty, risk and variation compared with a 2026 baseline.

The position will be hosted mainly in one of three Chalmers research environments: Geometry Assurance & Robust Design, Systems Engineering Design, or Automation. The exact placement will depend on the candidate’s profile and selected DRIVE PR research track. Together, these environments cover geometry assurance, robust design, systems engineering, product development methods, automation, autonomous systems and industrial digitalisation.

The rapid growth of freight transport challenges both infrastructure and the environment. Electrified waterborne transport offers a highly energy-efficient and climate-neutral alternative to road freight, particularly for coastal and inland waterways. This project focuses on advancing electromobility in shipping by integrating high-fidelity vessel operation simulations with energy system analysis to optimize performance, energy use, and infrastructure deployment.

Building on the VPF-IWV voyage planning framework and the ChaSE platform developed in previous projects at the Division of Marine Technology, the project will adapt electric propulsion and energy system modules for inland and coastal operations, including shallow, confined waterways and Swedish winter conditions. Route-specific simulations will quantify energy demand, assess battery performance, and evaluate operational reliability under environmental disturbances such as currents, waves, and ice.

The project will also perform life-cycle techno-environmental-economic assessment of alternative vessel concepts, comparing high-capacity ships and small-vessel platooning, while optimizing charging infrastructure location, power allocation, and utilization. By combining route-specific operational data, renewable energy integration, and electromobility assessment, the project will support the Swedish and the European shift from road to waterborne transport. The results will provide actionable guidance for industry adoption, policy development, and the deployment of resilient, low-emission, and electrified inland and coastal shipping systems.

This position is part of FiSRE (Towards Resilient 6G Networks, a Swedish and Finnish Joint Undertaking), a Vinnova-funded collaboration between leading Swedish and Finnish universities and industry partners. FiSRE aims to make resilience a built-in design property of 6G, alongside efficiency and sustainability, so that networks continue to deliver critical services not only during faults but also under hostile or degraded conditions, such as cyber threats, physical infrastructure damage, and extreme weather.

Your work focuses on the transport network layer that interconnects radio access, edge, and core. You will design and optimize resilience mechanisms that estimate disruption risk and proactively reconfigure the transport network to enable autonomous recovery, self-healing, and graceful degradation, drawing on machine learning where it strengthens these methods. The research supports mission-critical scenarios and feeds into an end-to-end resilience proof of concept developed together with Swedish and Finnish partners.

Your research will focus on the reproducible and benign synthesis of high-performance organic mixed ionic-electronic conductors (OMIECs) with optimal mechanical and electrical properties. In addition, you will contribute to the development of continuous flow chemistry processes. An example of our recent work can be found in a recent article in Science Advances. You will work closely with fellow PhD students and postdocs at Chalmers, and collaborate with partners across Sweden and internationally. This role also offers exciting opportunities to travel and engage with external collaborators.

This interdisciplinary project aims to address the wind industry’s “blades-to-landfill” challenge by developing sustainable, high-performance wind turbine blades made from wood veneers. Specifically, this project aims to develop a manufacturing process to create complex, aerodynamically precise geometries suitable for wind turbine blades using biobased materials. Additionally, the role involves utilising numerical simulations to investigate the structural and aerodynamic performance of the newly developed wind turbine blade under operational loads.

We are looking for two Postdocs with a strong background in functional programming to join our dynamic environment. We have two rather flexible subprojects, and we want to recruit the best functional programmers and tailor the exact research focus to your unique strengths.

Depending on your background and interests, you will have the opportunity to contribute to our ongoing research initiatives. These range from exploring the foundations of programming languages (such as advanced type systems and functional logic programming) to bridging the gap between ad hoc array processing and formally verified functional programming using interactive theorem provers.

Despite increasing research on car-free households, there is still a lack of understanding of how car owners and non-car owners differ in their mobility patterns, satisfaction, and use of mobility services such as car-sharing, cargo-bikes, and attitudes toward emerging technologies such as shared autonomous vehicles. This project addresses that gap through a mixed-method study combining survey data with in-depth interviews. An interdisciplinary approach, integrating systems and policy perspectives with behavioral psychology, will deepen the analysis.

This position is a 2-year Postdoc project. Sodium-ion batteries (SIBs) and their electrode materials, available on the market today, are to be investigated in their current maturity level. The SIBs hold a high potential to be a sustainable option for several applications currently using lithium-ion batteries (LIB). In this project, Sodium-ion batteries for transport: from materials to cell performance, the investigated SIBs and their materials are to be evaluated for transport applications. 

Core tasks include planning, preparing, conducting experiments, and analysing the results. The experimental work will be the foundation for the modelling work conducted in collaboration with researchers within the BMS team in the Automatic Control group. An existing model is to be further developed based on the experimental findings. You are expected to publish your results in journals and present them at international conferences.

(18) Postdoctoral Fellowship Position

Postdoc Fellowship Position summary/title: Postdoc in mathematics, with focus on educational science

Deadline :  August 15, 2026   

View details & Apply

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 Energy Technology and Policy group (ENTAP) is recruiting up to 2 postdoctoral researchers on technology foresight for policy-driven low-carbon technologies. The postdoctoral position will focus on developing data-driven approaches for probabilistic modelling of new technologies, both established technologies like solar and wind as well as emerging technologies like hydrogen. The positions will be linked to a number of national projects and embedded in the ENTAP group, with a strong international network and tradition of interdisciplinary collaboration through shared work spaces and a joint seminars.

The position is connected to the DYnamic populatioNs for resilient And equitable MObility systems (DYNAMO) project. The project develops next-generation synthetic populations that are dynamic, scenario-conditioned and suitable for stress-testing mobility systems under disruptions. The work combines mobile phone data, census and urban data, activity-based and agent-based modelling, generative sequence models, transport networks, digital twins, resilience indicators and equity-sensitive analysis. The project explicitly seeks to move beyond static “average-day” demand modelling towards dynamic, behaviourally rich and policy-relevant simulations of how different groups adapt under changing conditions. 

The project is carried out in collaboration withthe department of Architecture and Civil Engineering, Danmarks Tekniske Universitet (DTU, Denmark) and Universidad del Desarrollo / ISI Foundation (Chile/Italy), contributing expertise in human mobility modelling, resilience science, and empirical disruption analysis using large-scale mobile phone data from Chile.

This 2 year funded Postdoc project investigates how AI‑supported decision-making reshape collaboration, trust, and information sharing in a business-to-business context. Focusing on real-world supply chain management and logistics settings, it examines how trust in AI and between organizations influences data sharing, coordination, and joint efforts toward zero‑emission systems. 

Using qualitative and quantitative methods, the project develops and tests a practical framework for “trust calibration” in AI‑supported collaboration, helping organizations understand when to rely on AI, when to verify its recommendations, and how to design governance structures for responsible and effective collaboration. We have a pool of interview data to begin the project.

This project is a proof-of-concept effort aimed at demonstrating a reliable post-fabrication trimming process for Josephson junctions in superconducting qubits.

Unlike open-ended exploratory research, this project is goal-oriented and experimentally driven. It requires both careful physical interpretation and structured technical validation. You will take primary responsibility for planning and executing the experimental program in collaboration with senior researchers. While the role does not include formal personnel management, scientific initiative and independence are highly expected.

High-power charging is essential for the continued electrification of passenger vehicles, buses and heavy-duty transport. At the same time, demanding charging profiles, variable power demand and intensive operation create substantial challenges for the efficiency, durability and lifecycle cost of battery-supported charging systems.

This project investigates advanced reconfigurable and AC-native battery architectures for high-power charging infrastructure. The work will combine battery-system modelling, power-electronic architecture analysis, automatic control, and switching/modulation strategies. The postdoc will develop and evaluate models and control-oriented methods for flexible battery-based energy-storage systems.

The position offers an interdisciplinary research environment involving automatic control, electrical engineering, and battery systems. The postdoc will collaborate with academic and industrial partners in battery technology and electrified transport.

Many important processes in battery systems cannot be directly observed from routinely available measurements, which limits the accuracy, robustness and interpretability of existing models. This project aims to develop new physics-informed and data-driven methods for learning, modelling and predicting complex dynamical behaviour from limited measurement data. The work will combine system identification, state estimation, optimisation, scientific machine learning and physical battery modelling. The research will involve the development of new algorithms and computational methods, supported by theoretical analysis and validation using simulation, laboratory experiments and real-world battery data.

The postdoc will work in an interdisciplinary environment at the interface of automatic control, artificial intelligence and electrochemical energy storage. The position will also involve close collaboration with another postdoctoral researcher working on a complementary project in battery ageing analysis and diagnosis. Together, the two projects will connect methodological innovation with experimental and real-world battery applications.

This project is mainly funded by the Swedish Research Council. Through this project, the postdoc will have opportunities to collaborate with academic and industrial partners (e.g., Uppsala University and Volvo Trucks, ABB, and Husqvarna). Additionally, the postdoc may have the opportunity to be hosted by our group to apply for the Marie Skłodowska-Curie Postdoctoral Fellowship in 2026, for which we have a strong track record.

In this research environment, a postdoctoral researcher will be recruited within the Wireless Systems team to perform world-class research on 6G RDT-assisted situation-aware ISAC D-MIMO systems for resilient and efficient ITS and 6G-NTN. You will participate in interdisciplinary national and international research projects on 6G wireless systems with a special focus on proactive beamforming and resource allocation. 

In particular, you will be active within our new Chalmers Area of Advance Transport project 6G Digital Twin for Situation-aware Digital Infrastructure towards Resilient & Efficient Transport Systems (6GDiTS), and in our ongoing Swedish Research Council (VR) Research Environment Foundational Algorithms, Protocols, and Systems for Multi-Tier 6G-NTN Integrated Communication and Environmental Sensing (6G-NTN-E).

The appointment offers great opportunities to qualify for further research positions within academia or industry as we have numerous ongoing collaborations with the leading academic groups and industry both nationally and internationally, such as the EU Horizon Europe 6G-IA projects Smart, Automated, And Reliable Security Service Platform for 6G (ROBUST-6G), and Efficient Confluent Edge Networks (ECO eNET), and within our research center Wireless Infrastructure Technology (WiTECH ) at Chalmers, in particular within the project “Distributed communication and radar sensing convergence” (DisCouRSe).

Future automated transport systems will increasingly rely on digital infrastructure to support safe vehicle localization in challenging urban environments. Cellular infrastructure offers a promising complement to satellite-based positioning, but safety-critical applications require more than accurate estimates. In this project, you will develop novel methods for trustworthy infrastructure-assisted positioning using cellular observations from roadside infrastructure. You will investigate how model mismatch, distribution shifts, calibration errors, and corrupted measurements affect localization performance, and develop hybrid solutions that combine model-based estimation, machine learning, and integrity monitoring. The project, funded by Chalmers Area of Advance Transport, is conducted in collaboration with researchers in wireless sensing, intelligent transportation systems, AI security, and trustworthy machine learning at Chalmers and AI 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.

The project aims to develop novel AI and machine-learning methods for accelerated materials discovery. The focus is on combining generative AI, active learning, first-principles simulations, and machine-learning potentials to identify new multicomponent optoelectronic materials for energy-conversion applications.

The successful candidate will contribute to the development of an autonomous materials-discovery framework capable of navigating extremely large compositional spaces while simultaneously optimizing multiple target properties such as optical performance, stability, and sustainability.