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

We are looking for a Machine Learning Specialist with an emphasis in Computational Biology. In this role, you will be part of an interdisciplinary team studying human development and disease processes through understanding different cell types and their functions. The successful candidate will work in the Lagergren group, CST KTH, on the development of Variational Auto-Encoders (VAEs) to effectively construct lineage trees in collaboration with the Frisén group, KI, which will use the innovative LUSTRE technique to generate data. You will be performing tasks such as: (1) Develop, optimize, and apply machine learning methodologies to decipher complex biological data derived from techniques like LUSTRE and Smart-seq3. (2) Collaborate with the team to develop and fine-tune Variational Auto-Encoders (VAEs). (3) Assist in developing distance-based and Bayesian methods for lineage tree reconstruction based on VAE computed distances or latent variables. (4) Analyze datasets from human blood and brain cells and cancer.

The position is part of a collaborative project with the Division of Wood Science and Engineering at Luleå university of technology, whose overall goal is to develop theory and algorithms for image-guided optimisation of the sawline. Specific tasks is to develop and implement physics aware deep neural networks for tomographic 3D image reconstruction with partially unknown acquisition geometry. Prototypes will be implemented as components in ODL (http://github.com/odlgroup/odl) that offers a seamless coupling between frameworks for tomography (ASTRA) and deep learning (PyTorch).

This Postdoctoral position aims to develop advanced predictive modeling techniques to forecast energy demand accurately, optimize energy consumption, and enhance power systems’ overall operation using advanced control strategies, Artificial intelligence (AI), and Machine learning (ML). The successful candidate will have the chance to work with state-of-the-art AI algorithms and develop innovative solutions for predicting energy patterns, identifying anomalies, and recommending optimal energy management strategies. Moreover, this research will contribute to the development of intelligent systems capable of adapting to dynamic energy demand and supply scenarios, thereby improving system reliability and efficiently using available resources. The knowledge and expertise gained from this research will contribute to resolving practical applications in various energy sectors, including renewable energy integration, smart grid management, and sustainable energy planning. The goal is to utilize control, AI, and ML to unlock new possibilities in predictive modeling for energy management and power systems engineering, ultimately paving the way for a more sustainable and efficient future.

The position is located at the division of process technology at KTH and will aim at contributing with simulation to new decarbonized and sustainable processes for industry. The present post-doctoral project focuses on CFD simulations of chemically reacting flows with application to industrial reactors and heat recovery. We consider the flows, turbulence, mixing, finite rate chemically reaction and heat-transfer in the simulations. We use high-fidelity simulations to capture the details of the chemical reaction inside complex flows. The application will be (i) simulation of crystallization (using Population Balance Equation) and (ii) simulation of waste-heat recovery using dissociating gases. The work consists of simulation using super-computers, code modification/implementation of new models, data analysis and presentation of the result. The data analysis might include some coding in python for creating/modifying short scripts for using Machine -Learning or modal analysis techniques.

We are currently looking for two postdocs that will conduct research within the WASP NEST project entitled “Learning in networks: structure, dynamics and control”. The project aims to develop novel mathematical and computational tools for the de­sign of algorithms learning the structure, the dynamics, and the control of large­scale networks. The objectives are two­fold: (i) to develop a comprehensive theory of learning in networks, and (ii) to apply our results to two application domains, namely decentralized and automated network management of future radio communication networks, and online interme­diation platforms. The position and employment will be at KTH Royal Institute of Technology, School of Electrical Engineering and Computer Science (EECS), located in Stockholm, Sweden. The research will be supervised mainly by Prof. Alexandre Proutiere (KTH) and Asst. Prof. Fiona Skerman (Uppsala University), but with opportunities to work with the other co-PIs of the project who are Prof. Anders Rantzer (Lund University) and Assoc. Prof. Emma Tegling (Lund University). 

Graph generative models (GGM) are  gaining significant interest in different application domains. They are commonly used to model social networks, knowledge graphs, medical data and bio/chemical networks. Large pretrained models like LLMs dominate already the generation of text and inspire the training of equally large graph pretrained models for respective domains. Recently multi-modality in LLMs is becoming popular with impressive results in the area of image, video and sound generation. On the other hand GGMs can also benefit from such approaches in domains where graph generation is needed including bio/pharma/medical applications (i.e. medical graphs/protein generation), electrical or communications networks, social networks etc. Thus, the work area for the post-doctoral researcher will be to design and innovate in this area with contributions to graph pre-trained models and in multimodal graph encoders and graph decoders. The issues of graph pre-trained strategies will be part of the research. Different models of generators will be considered including auto-regressive, one shot (i.e. GANs,  GAEs) or diffusion based ones. The post doc tasks will include collaborative research with the faculty and the PhD students, participation in methodological design and experimental parts, writing up papers and proposals for fund raising. Anticipation, initiative and creativity are crucial for this position.

You will contribute to the development of methods for protein design through close interaction with leading partners at KTH and Chalmers at the forefront of biocatalysis and synthetic biology. The research will then be presented at international conferences and in international peer-reviewed journals. The postdoctoral duties include:

• Computer-based design of biocatalysts for selective generation of food additives
• Molecular modeling/simulation followed by in silico enzyme engineering to obtain enzymes capable of generating tailored fat molecules
• Development of bioinformatics methods for tailoring enzymes
• Research carried out in this area is conducted at low TRL levels, ie below 4, which means that the research is of a fundamental nature
• Actively contribute to the research group’s activities, for example through supervision of bachelor / master students 

The Division of Network and Systems Engineering at the Department of Computer Science in the School of Electrical Engineering and Computer Science is looking for a postdoctoral researcher in the area of resilient learning and edge computing. With a focus on multi-agent learning in the context of edge computing, we are particularly interested in addressing fundamental problems related to resource management, distributed detection, learning equilibria in games, and learning in adversarial environments. Of interest are also approaches leveraging state-of-the-art neural architectures, including graphical models and federated learning. We will use the developed models for the design of topologies and learning algorithms that are resource efficient and are resilient to noise and to adversarial behavior. The methodological results will be applied in a variety of critical infrastructures, from smart grids through building automation to railways.

The department of Mathematics is offering a two-year postdoc position. The research project will be focused on the development of computational methods that can be used to effectively simulate the dynamics of many bubbles in two-phase flow simulations. An example is the simulation of microbubbles. Microbubbles can for example be used to transport drugs to the desired location in the body. We will develop a micro and a macro model that can be used to scale up simulations with a few drops to many thousands of drops. The project span theory, method development, implementation, and simulation.

Research in collaboration with medical doctors for patient data analysis using machine learning has a high impact on public health. Medical caregivers such as doctors and nurses need to make important decisions based on observed patient data. Often the data is complex. Therefore, our objective is to use machine learning to assist the decision of medical caregivers.  We show one example: 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. The postdoc will directly interact with doctors at the world-renowned Karolinska Institute and Hospital. In addition, the postdoc will develop new machine learning methods for life science data analysis, such as gene regulatory networks and metabolism networks. This will be in collaboration with SciLifeLab at Stockholm.

We are looking for a postdoc to a multi-disciplinary project in generative AI. The position is directed towards methods for fine-grained recognition of human non-verbal behavior in video using 3D models and generative AI. Together with students, these models will be applied to both medicine (recognition of early symptoms of motor and cognitive diseases) and interaction design (affect recognition, modeling of music communication). The postdoc will be placed in the division of Robotics, Perception and Learning at KTH, a dynamic and internationally successful environment with high equality and diversity. The postdoc will participate in the daily work at the division and research group, including reading groups, seminars and co-supervision of PhD students.

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.

A fully funded postdoctoral position is available in a new robotics research group at KTH that explores how to adapt recent advances in AI/ML to improve the capabilities of real (fielded) autonomous robots acting in real-world workplaces and public spaces (mapping, planning, interaction). Join us to develop the next generation of AI-enhanced autonomy stack for fielded robots in complex real-world environments. We have a cutting-edge Boston Dynamics’ Spot with the Spot Arm for real-world experiments. The group is led by Assistant Prof. Olov Andersson at the Division of Robotics, Perception and Learning. Previously a senior researcher in the Autonomous Systems Lab at ETH Zurich, he was part of the winning team in the recent DARPA SubT Challenge on autonomy in unknown environments:

Vår grupp arbetar med storskalig bildbaserad kartläggning av människans celltyper, i nära samarbete med Human Protein Atlas projektet. Vi söker en postdoc för att vara del i vårt arbete att utveckla nya tekniker för högupplöst spatiell kartäggning och spatiotemporal manipulering av proteiner i mänskliga celler. Detta arbete kommer innebära utveckling av antikroppsbaserade tekniker, molekylärbiologiska tekniker, mikroskopi och maskininlärningsmodeller för att analysera bilddata och annan spatiell data.

 

(18) Postdoctoral Fellowship Position

Postdoc Fellowship Position summary/title: Postdoc in Mathematics, production optimization project with AstraZeneca

Deadline : 14.Aug.2023

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The advertised post-doctoral position is within an ambitious Digital Futures ISPP Project titled “Smart Predictive Maintenance for Pharmaceutical Industry (SMART)”, led by KTH and AstraZeneca.  In this industrial post-doctoral project, you will develop, evaluate, and implement novel methods to gather manufacturing data in a production environment. You will use your expertise in machine-sensor technologies, IoT, and operation condition monitoring to carry out your research.  In the SMART project, you will work on-site in the AstraZeneca manufacturing facilities in Södertälje ~50% of the time with a goal to start in October 2023. This will afford you the unique opportunity to gain firsthand experience in an industry setting while also contributing to research of others in your cohort. Ultimately this should culminate in multiple prospects for co-authorships and patent applications, while delivering a minimum viable product (MVP) that increases the overall equipment efficiency (OEE) of the lines. Please see openings for the other SMART-project positions here and here

The advertised post-doctoral position is within an ambitious Digital Futures ISPP Project titled “Smart Predictive Maintenance for Pharmaceutical Industry (SMART)”, will be led by KTH and AstraZeneca. In this project, you will focus on human-centric technological interventions in order to inform the creation of sensor networks and the parametrization of machine learning plant maintenance predictive models. This project will be led by Associate Professor Mario Romero. You will lead the creation of peripheral visualization interfaces to enhance plant operators with the information generated by the sensor networks and predictive systems. In the SMART project, you will work on-site in the AstraZeneca manufacturing facilities in Södertälje ~50% of the time with a goal to start in October 2023. This will afford you the unique opportunity to gain firsthand experience in an industry setting while also contributing to research of others in your cohort. Ultimately this should culminate in multiple prospects for co-authorships and patent applications, while delivering a minimum viable product (MVP) that increases the overall equipment efficiency (OEE) of the lines. Please see openings for the other SMART-project positions here and here

We are looking for a highly motivated Postdoctoral Fellow in Biocatalysis and Enzyme Engineering to join a young, independent research group in the Department of Industrial Biotechnology. Our interdisciplinary research efforts aspire the development of next-generation biocatalysts for green chemistry. By joining our group you will develop innovative biocatalytic solutions for a wide range of highly relevant organic syntheses. Your research activities will advance new enzyme-catalysed transformations that you will implement in the synthesis of fine chemicals, pharmaceuticals, and new modalities, enabling applications in chemical manufacturing platforms.

The Nonlinear and Quantum Photonics group  (https://www.aphys.kth.se/qbp/nqp) at the Department of Applied Physics is offering a postdoctoral research position to develop chip-scale lithium niobate and silicon photonic integrated circuits for quantum optics applications. The position is supported by the Knut and Alice Wallenberg Foundation to promote Swedish-Finnish cooperation in the area of quantum technology and involves collaboration with VTT-Finland in synergy with WACQT, the Swedish initiative for quantum technology. The project offers the opportunity to develop cutting-edge technology for integrated and quantum optics on state-of-the-art silicon and lithium niobate photonic platforms, working in an international and collaborative research environment.

The Science for Life Laboratory (SciLifeLab) is a national center for molecular biosciences with focus on health and environmental research. The combination of the highly interdisciplinary expertise and research projects at the department is unique in Sweden and also at an international level. This expertise ranges across cell biology, biochemistry, biophysics and theory. Recently, the SciLifeLab and Wallenberg National Program started the Data-Driven Life Science (DDLS) program in order to accelerate life science research through innovations in AI and data science. The AICell Lab (https://aicell.io) in the department of Applied Physics at KTH and SciLifeLab is a new research group funded by the DDLS Fellows program. The group, led by Wei Ouyang, focuses on building AI systems for data-driven cell and molecular biology. To expand the team, we are now seeking a postdoctoral researcher to develop AI-powered automated microscopy imaging systems. The aim of the project is to build a smart microscopy imaging farm for massive production of image data. It consists of multiple microscopes and fluidic systems, robotic arms, liquid handling robots and automatic incubators. The farm will be used for performing automated widefield/fluorescence imaging, long-term live cell imaging, tracking,  spatial-omics and multiplexing imaging. With the AI-powered control software, data are analyzed in real-time, augmented views are added on the fly. By generating feedback control signals to control the microscope, the software will automatically change field-of-views, illumination power and other experimental conditions in order to optimize the phototoxicity and capture rare events in live cells.

The AICell Lab (https://aicell.io) in the department of Applied Physics at KTH and Science for Life Laboratory is a new research group funded by the Data-driven Life Science Fellows program. The group, led by Wei Ouyang, focuses on building AI systems for data-driven cell and molecular biology. To expand the team, we are now opening a position for a postdoctoral researcher in cell biology and computational microscopy imaging. The aim is to develop wet lab protocols and analysis workflows for an AI-powered automated imaging system to actively detect, monitor and track the live human cells under drug and genetic perturbations. The project is supported by the WASP DDLS collaboration, and in close collaboration with the Jaldén group at KTH and the Lundberg group under the Human Protein Atlas, which is a unique world-leading effort to map all the human proteins in cells, tissues, and organs in the human body. You will be responsible for actively and effectively designing, implementing, and optimizing wet lab experiments for the self-driving microscope, including cell culture, sample preparation and microscopy imaging. Importantly, you will also actively work with other researchers in the group to develop and optimize protocols for the automated imaging system and AI models. Besides wet lab skills, you will get the opportunity to elaborate on your computational skills, e.g., by writing Python scripts to control the imaging farm, train AI models and develop automated data analysis workflows. In later stages of the project you will also design and conduct additional, custom validation experiments, which may involve different molecular biology techniques.