07 Postdoctoral Fellowship at Stockholm University, Sweden

The position will be associated with the project “Porous materials development for carbon capture and separation under ambient conditions”. The project aims to  explore and engineer porous materials for application at atmospheric conditions.

The position involves research in the synthesis and modification of porous materials (including but not limited to zeolites, silica, MOFs, or activated carbons), and study of their adsorptive behaviors and applications, such as CO2 adsorption and separation from ambient air. In addition, the position will supervise junior researchers and take care of the laboratory routine.

The applicant will be placed in the Astroparticle and Elementary Particle Physics division at Stockholm University. The division is part of the ATLAS collaboration, and is deeply committed to the operations and upgrade of the experiment, as well as to the analysis of the data collected. The announced position will focus on the luminosity program of the ATLAS experiment, in particular the online activities.

The successful candidate will develop machine learning (ML) strategies, primarily revolving around interpretable ML and generative AI, to study complex biological processes. This project combines timely analytical challenges with deep rooted applications in life science. We are looking for a candidate with a PhD in either engineering/computer science/physics/mathematics. Experience with ML implementation (ideally interpretable ML and/or generative AI) is required.

The Selected postdoc will be joining the Computational Microscopy for Cell Biology (CMCB) lab led by Dr. J. Griffié, which is primarily composed of engineers, physicists and mathematicians. The lab is based at SciLifeLab in Stockholm, a vibrant and dynamic science hub, with a particular focus in multidisciplinary research. The CMCB laboratory aims more specifically at developing cutting-edge data/image analysis as well as modelling strategies to answer fundamental biology issues with advanced light microscopy data. The lab’s research scope ranges from reinforcement learning for drug design, interpretable ML pipelines for cancer research and diagnosis as well as graph neural networks for microscopy.

We are seeking a postdoctoral researcher for our interdisciplinary project aimed at developing and applying advanced machine learning techniques to recognize and classify submarine glacial landforms. This position is ideal for a scientist with expertise in machine learning and a strong interest in environmental or geoscientific applications. You will work with high-resolution seafloor terrain models, primarily collected via multibeam sonar systems, to develop robust computational tools for analyzing large geophysical datasets. You will:

• Apply advanced machine learning methods (e.g., supervised learning, clustering, neural networks) to classify submarine landforms.
• Collaborate with geomorphologists to ensure geological relevance and validate model results.
• Contribute to developing scalable tools for analyzing marine geophysical data, aiding predictions of glacial ice dynamics under climate change.

We are seeking a postdoctoral researcher for our interdisciplinary project focused on understanding submarine glacial landforms and their implications for glacial ice dynamics and ocean systems. This position is ideal for a geoscientist with expertise in submarine geomorphology and glacial geology as well as experience in working with marine geophysical mapping data. You will analyze high-resolution seafloor terrain models collected via multibeam sonar systems, interpreting these features to advance our knowledge of ice dynamics, ice-ocean interactions and the processes shaping the seafloor. You will:

• Collaborate with machine learning specialists to develop models for automatic recognition and classification of submarine landforms.
• Provide critical geological insights to validate and refine machine learning outputs
• Contribute to understanding how glacial ice moves, melts, and impacts ocean systems under climate change.

A postdoctoral fellow with data science expertise is being recruited to lead the exploration of large nontarget mass spectrometry datasets by multivariate techniques and machine learning approaches. The successful applicant will contribute to the development or application of in silico tools to meet scientific objectives in a major national project, Mistra SAFECHEM, and in the international EU project, INQUIRE. In these projects we have generated large strategic datasets from the systematic non-target analysis of air and water samples using high-resolution mass spectrometry. A data scientist is now needed to use data-driven approaches for the identification of hazardous substances based on the analytical data, spatial patterns, and sample toxicity data from our collaborators. The overall aims of these projects are to discover and reduce hazardous chemicals in our homes and in the ambient environment.

The Trapped Ion Quantum Technology Group at Stockholm University is seeking a highly motivated postdoctoral researcher to contribute to pioneering research in open-system quantum simulations with trapped Rydberg ions. This position is part of the ERC Synergy project “Open 2D Quantum Simulator (OPEN-2QS)”.

The OPEN-2QS project aims at developing a 2D quantum simulator for open system quantum dynamics using trapped ions excited to Rydberg states. This novel approach features fast interactions combined with long-term stability. This will enable the simulation and study of phenomena such as novel states of matter like quantum glasses, molecular dynamics and quantum thermodynamics.

The project is primarily experimental and will be carried out within the Trapped Ion Quantum Technology Group at Stockholm University. It will involve close collaboration with experimentalists and theoreticians at the Universities of Mainz and Tübingen within the framework of the ERC Synergy project