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

Are you looking for a postdoc combining academic research in machine learning with close collaboration with the industry? Do you want to join our research group at KTH developing fundamental machine learning times series algorithms and the team at Getinge producing the next generation ventilators for the intensive care? Are you passionate about physical simulation and proficient in parameter estimation? If so, we believe you will find our project developing a digital twin of the respiratory system a perfect match.

This project is co-funded by Vinnova, within the program Advanced Digitalization, and Getinge. Getinge is a world leading MedTech provider with 12.000 employees worldwide. The company has a long tradition of creating innovations that save lives. Their Critical Care Product Area is based in Stockholm, Sweden, and you will interact with its Innovation team regularly during this project. The close collaboration with this multi-disciplinary team of experts is a valuable opportunity for you to create a bridge between academia and industry, in an open and collaborative way. This is also a unique chance that will open multiple future career paths for you.

The research project concerns the development and use of MINFLUX microscopy for cellular imaging and dynamic studies on the nanoscale. The project is conducted at the Advanced Light Microscopy facility (ALM, Integrated Microscopy Technologies – SciLifeLab) at Science for Life laboratory. The project takes place in collaboration with researchers from KTH, KI and SU with the goal of analysing cellular processes on the nanoscale, especially in the compact three-dimensional cell nucleus. Project aims and tasks include development of adequate single-molecule labeling strategies, optimised use of high-precision MINFLUX microscopy, and establishing methods for data precision analysis. 

Gene therapies based on viral vectors are on the rise, and in recent years several new drugs based on adeno-associated viruses (AAV) have reached patient and market approvals. A challenge to realizing the availability of these life-changing advanced therapies is to simplify and improve their manufacturing process.

We are therefore currently looking for a researcher who will work with a team to lead, research and lead the development of refined mammalian cell lines for the production of biological drugs, such as AAV, protein and antibodies. The person we are looking for will have tasks that revolve around OMICS, genome changes with CRISPR/Cas9, protein and virus production in mammalian cells as well as their purification, quantification and analysis. The person will work with various methods such as NGS sequencing, molecular cloning, ELISA, protein expression and affinity determinations.

KTH is developing a third-generation VLSI design style called Synchoros (not a typo) VlSI design style. SiLago (Silicon Lego), a Lego-inspired design framework, has been built with this design style as the basis; see this video for an introduction. A heterogenous CGRA fabric with a parallel distributed control scheme is the micro-architectural basis for the SiLago framework. This post-doc position will enhance an existing synthesis framework by incorporating it into the MLIR framework and enhancing its synthesis engine to target systolic arrays and SIMD architectures built with the heterogeneous CGRA fabric. Such a synthesis engine will be used to build a library of algorithm implementations for AI/ML in varying dimensions and degrees of parallelism and explore design space at application and system levels, where application is defined as a hierarchy of algorithms and system as concurrent and communicating applications.

In collaboration with IMEC, KIT, LIRMM, and EPFL, KTH is developing a design flow for implementing digital designs in an innovative and challenging monolithic 3D integration technology. Over the years, KTH has developed a Lego-inspired design framework to enable composition by abutment to create timing and DRC clean designs. The postdoc will adopt composition by abutment to implement elementary to complex micro-architectural blocks in terms of atomic bit-slices and interconnect elements. The design and its cost model should be composable and able to predict latency and power consumption accurately. The developed design method will be manual initially but amenable to automation later in the project.

In this project, you will work on the most advanced wireless communication technologies for future mobile networks (6G) such as machine learning (e.g., reinforcement learning, large language models), hyper low latency and high reliable communications, extremely large MIMO. You will have the opportunities to collaborate with top researchers and industries in the area. You are expected to work on the theoretical analysis of communication systems and also large-distributed learning systems. The project will be hosted by the Division of Information Science and Engineering.

In the Department of Robotics, Perception and Learning (RPL), research is carried out in computer vision, robotics and machine learning. We are now looking for two postdocs in robotics and machine learning and computer vision. The successful candidates is expected to conduct research at the highest international level and supervise doctoral and master’s students.

The goal of this project is to enhance the development and implementation of wave energy convertor technology by integrating tribological principles into the wave energy convertor design work. The project includes activities related to the systematic collection, organisation and presentation of existing knowledge as well as fundamental research to address critical knowledge gaps with a particular focus on bearing design, selection and operation in wave energy convertor systems. The research work is in close collaboration with industry and international researchers.

The Chemical Sensing Group is a large initiative at KTH, with approximately 20 researchers. We develop fundamental electrochemical and optical concepts to be integrated into ‘chemical sensors’ aiming to solve current problems in healthcare, environment, wellness, food among others. The objective of the postdoctoral positions is to strengthen the research on electroanalytical concepts and fundaments at the Chemical Sensing group.

We are currently looking for two Research Associates (Post Docs) within the area of Machine Learning and wireless communication networks. The Research Associates will be sponsored by the research project of the Swedish Foundation for Strategic Research (SSF) AICOM – Artificial Intelligence for Wireless Communications, and the research project of the Knut och Alice Wallenbergs Foundation (KAW) TAIRCOMP – Turning the Air into an AI Computer, two recent projects of the highest scientific quality, which involve leading Swedish universities and scholars.

We are looking for a postdoc to work on the broad topic of synthetic biology for carbon fixation. The group has several ongoing projects in this area using both in vitro and in vivo systems. A strong focus is on the genetic manipulation of photosynthetic cyanobacteria with the purpose of enhancing carbon conversion.

The postdoc will be active in multiple steps of the synthetic biology workflow. This includes design and construction of bacteria strains containing novel enzymes and metabolic pathways, using for example CRISPR gene editing. Novel enzyme sequences are generated using various bioinformatic and physics-based modeling tools. Mutagenized bacteria strains will be characterized using high-throughput screening technique.

The postdoc will join a diverse group with backgrounds in bioinformatics, molecular biology and plant science. External collaborators for this project include other research labs in Europe as well as industrial partners.  The postdoc is expected to help train incoming lab members in molecular biology techniques.

Research in collaboration with medical doctors for patient data analysis using machine learning has a high impact on public health for the vision of Digitalization in Health Care. Medical care givers such as doctors and nurses need to take important decisions based on observed patient data. Often the data is complex. Therefore, our objective is to use machine learning for assisting the decision of medical caregivers.  We show two concrete examples as postdoc projects:

1. How to predict and detect an infection before visible symptoms like fever? When infections start, body parameters like breathing pattern, oxygen content, heart rate, pulses, etc. start changing. Then, can we track the body parameters and predict the infection buildup? If we can, then it saves time for medical intervention much before visible symptoms.
2. Medical doctors often face difficulty to choose a set of medicines from many options available for a patient. Medication is expected to be disease-specific as well as person-specific. Individual patients may respond differently to the same medication, so the selection of medication should be personalized to everyone’s needs and medical history. The question is: how AI can help doctors to identify existing medications and/or therapies that can be repurposed for the treatment of a disease? In this scope, we work with dementia patients.

This position is linked to the EU project Blade2Circ, which aims to develop and test the next generation of bio-based wind turbine blades. There has been a strong expansion of wind power in Europe and Sweden in particular, and wind power is considered to be one of the most important future sources of renewable energy. However, the management of wind turbine blades at the end of their service life is a challenge that has not yet been resolved. Today, a large part are placed in landfills and the rest is used in energy recovery. The duties of this position consist of generating a set of EoL scenarios and strategy paths for materials and products by representing and modeling the value chains taking into account multidisciplinary factors (regulatory, social, safety and operational) and decision-making structures that affect the flow of materials. Data collection will be carried out with both site visits and interviews to ensure that the modeled system represents the operational scenario for EoL routes. In addition to this, participation in the project’s joint activities and deliveries is also included. In addition to participation in the project, the position includes 20 % institutional service, primarily in the form of teaching at basic and advanced level