07 Postdoctoral Fellowship at KTH Royal Institute of Technology, Stockholm, Sweden

We have witnessed spectacular successes including Nobel prize in developing artificial intelligence (AI) methods for estimating complex biological structures such as protein structures. This is achieved by harnessing power of deep neural networks (DNNs) and generative AI (GenAI). The AI methods typically use supervised learning that requires a large amount of labeled data. For example, AlphaFold used protein sequence-and-structure as labeled data kept in Protein Data Bank. In addition, the protein sequence is a (relatively) clean data without much noise.

There are abundant unlabeled and noisy data in research fields of modern biology and medical science. Naturally, estimating biological structures and networks from unlabeled and noisy data using unsupervised and semisupervised learning widens the scope of future AI-based research in biology. This has directly actionable effects in medical science. The major technical challenge is development of robust AI methods that can use information hidden in unlabeled and noisy data. A promising path to address the challenge is to include a-priori biological knowledge in developing models for signals and systems, and collecting data, and then regularize the learning of AI methods.

This postdoc project will develop biology informed robust AI and generative AI methods that can use the abundant unlabeled and noisy data. We will focus on inference of gene regulatory networks (GRNs) from their noisy gene expression level data – a challenging inverse problem in biology. The project is challenging and part of Digital Futures Flagship project “Biology-informed Robust AI Methods for Inferring Complex Gene Regulatory Networks”. The postdoc scholar will work independently and in collaboration with Prof. Erik Sonnhammer’s group at SciLife Lab and Assist. Prof. Martina Scolamiero at KTH Mathematics department.

Successful candidates will conduct research in the area of causality and machine learning methods for human mobility behavior modeling and transportation systems with Liam Solus (Department of Mathematics) and Zhenliang Ma (Department of Transportation Planning).  Applicants with a background in causality, machine learning, statistical learning, and statistical methods for transportation research are encouraged to apply.  The successful candidate will be a member of the Department of Mathematics at KTH, which has active groups in mathematical statistics, applied mathematics and related fields. The project is a joint collaboration between faculty in the mathematics and transportation departments.   So the successful candidate will also be included in  the Transportation Planning Division and Digital Futures Program at KTH. The position is time-limited, full-time, two year position starting 25 August 2025 or at an agreed upon date (either earlier or later).  The position in 80% research responsibilities and 20% teaching responsibilities. The position is financed through the KTH Digital Futures Program.

This advertisement is for a two year postdoctoral position to work on the problem of flow electrification. The position is hosted jointly by KTH through Digital Future and NORDITA. The project is financed by Hitachi Energy. 

Power transformers are a key component in the electrical system. Typical power transformer rating is more than 100 MVA. The efficiency is approximately 99.8 percent. The losses are due to resistive heating (thermal losses) and require an effective cooling done by oil flow through the electrical insulation close to the windings. Here we have the potential risk that the oil, while flowing through ducts made of cellulose, can acquire static charges that can lead to dielectric breakdown. The goal of this project is to understand this phenomena – streaming electrification – through careful, controlled, laboratory experiments (done in parallel at Uppsala University) and theory. 

The applicant will join the ongoing ERC-funded project ‘intimate touch’ which seeks to produce design exemplars, methods and datasets that can support designers and engineers in creating technologies that use touch to interact with us. The applicant will join an interdisciplinary team that is designing and building novel shape-changing, soft robotic and haptic technologies that touch the body, including parts of the body that are sensitive and taboo to touch. The team is now looking for a post-doctoral researcher who can help to lead lab studies as well as in-situ studies to help evaluate our design work. We aim to create transferable datasets for designing touch interactions that can be used by other engineers and designers. Ideally the applicant would have a background in psychology or neuroscience and would have previous experience in utilizing creative approaches in their work.

We invite applications for a two-year Postdoc position within the ERC project DynanoNet, which aims to develop materials, methods, and technologies that enable adaptive separation of molecules and particles using dynamically tunable nanofibril networks. You will perform coarse-grained molecular dynamics simulations to study the transport of solvents, molecules, and particles through nanofibril networks and investigate the influence of network topology.

You will use and compare two approaches: (1) an ESPResSo coarse-grained model developed at KTH providing high spatial and temporal resolution to capture detailed particle–network interactions and (2) a supra coarse-grained model combined with the lattice-Boltzmann approach developed at the laboratory of organic electronics (LOE) at Linköping University. You will collaborate within the DynanoNet project to combine experiments and simulations.

Your principal supervisor will be Dr. Tobias Benselfelt, with assistance from Dr. Ahmad Reza Motezakker at KTH and Dr. Aleksandar Mehandzhiyski at LOE. In addition, you will work in a world-leading focus group on simulating nanofibril networks, including Dr. Ahmad Reza Motezakker, Dr. Tomas Rosén, Professor Jakob Wohlert, and Professor Daniel Söderberg at KTH, and Dr. Aleksandar Mehandzhiyski and Professor Igor Zozoulenko at LOE. You will assist in coordinating this collaboration.

We are looking for a postdoctoral fellow to develop and improve models and codes for assessment of the liquid source term during severe accident (SA) of LWR systems. In particular, research will include assessment of the characteristics of the containment water pool by system code, thermodynamic and kinetic aspects of Cs and Sr chemistry including model development and its implementation to the system codes. This project is contributing to the larger EU project SOCRATES which addresses the critical gaps in understanding of the liquid source term during SA and offers innovative solutions to mitigate and monitor the release of radionuclides into the environment.