Postdoc position: AI methods for large-scale microscopy imaging

We are currently looking for an ambitious, highly motivated Postdoc with a good background in AI and imaging to join the HASTE project.

This is a 2-year postdoc position. Assignments include development and application of methods for large-scale analysis of microscopy images using AI / Machine Learning within the framework of the HASTE project. The project focuses on AI / machine learning with quantifiable confidence or probability, based on methods such as Active Learning, Conformal Prediction, Probabilistic (Venn) Prediction, and Deep Learning. Applicants are expected to collaborate with other project members and participate in regular research visits with industry partners AstraZeneca and Vironova.


PhD degree or a foreign degree equivalent to a PhD degree in a relevant field. The PhD degree must have been obtained no more than three years prior to the application deadline. The three year period can be extended due to circumstances such as sick leave, parental leave, duties in labour unions, etc. Documented experience with AI / ML methods and / or computerized image analysis. Experience in programming in eg Python is a requirement. Applicants should have excellent communication skills and be keen to actively interact with other team members including biologists, systems developers and researchers in AI / ML. Furthermore, applicants should be curious and creative, take initiatives and build relationships. Applicants should have good organizational ability, be able to structure work with multiple projects and solve anticipated and unexpected problems. The applicant must be able to express themselves very well in written and oral English

Apply via link at the bottom of the University application: (Please note: You MUST apply to the position via the form at Uppsala University, do not send any application documents to Ola Spjuth by email.)

If you have any questions regarding the project, please contact group leader Ola Spjuth.

Deadline to apply: May 7th, 2020

Hongru Zhai joins the HASTE team to work on the developing hierarchical representation of the microscopy image data


Hongru is a master’s student from the department of statistics, and his main interests in statistics include multivariate statistical methods and Bayesian statistics.

Hongru’s MSc thesis will focus on developing the better hierarchical representations of the microscopy image data from cellular experiments with the help of statistical methods, focusing on improving readability and informational efficiency of the representation.

Tianru Zhang joins HASTE team as new PhD Student to work on management of large data streams

We welcome Tianru Zhang as new PhD Student in the Hellander lab at the Department of Information Technology, Uppsala University.

Tianru obtained his Bachelor in Probability and Statistics in Mathematics at the University of Science and Technology of China in 2017. Then, he completed his Master in Statistics for Smart Data at ENSAI (The National School of Statistics and Analysis of Information of France) in 2018. Before moving to Uppsala, he was employed as Assistant Researcher at the Fujitsu R&D center Co., Ltd. where he worked on developing DeepTensor (a deep learning method using tensor decomposition) and analyzing data of personal online loans.

Andrea Behanova joins the HASTE team to work on the enhancement of image quality by registration of short exposure miniTEM images

Andrea is doing a traineeship at the Department of Information Technology – Division of Visual Information and Interaction, Uppsala University while coursing the last semester from the master’s in Medical Physics at the University of Eastern Finland.

Andrea’s internship project focus on developing an approach of registering and aggregating short exposure miniTEM images (superresolution reconstruction). The project objective is to achieve better quality and higher resolution image compare to a long exposure one.

Ebba Bergman joins HASTE team as PhD student

We welcome Ebba Bergman as new PhD Student in the Spjuth lab at Department of Pharmaceutical Biosciences, Uppsala University.

Ebba obtained her Master of Science in Engineering, with a focus on bioinformatics, from Uppsala University in 2017. Before starting her PhD Ebba worked as a full-stack systems developer for 2 years. 

About the PhD project within HASTE:  
Ebba is currently working on Conformal Prediction (CP) in combination with Convolutional Neural Networks. Next, she will focus on applying Learning Under Privileged Information (LUPI) on transmission electron microscopy data provided by Vironova. In general, Ebba will focus on combining machine learning methods with CP and LUPI using data provided by our HASTE-project partners.

Ankit Gupta joins HASTE team as PhD student

We welcome Ankit Gupta as new PhD Student in the Wählby Lab at the Department of Information Technology, Uppsala University.

Ankit obtained his Bachelor’s in Electrical Engineering at Indian Institute of Technology Indore in 2014. Then, he completed his Masters in Medical Imaging and Informatics at Indian Institute of Technology Kharagpur in 2017. Before moving to Uppsala, he was employed as Research Engineer at the University of Bern where he worked on developing a video-based instrument tracking system in stereoscopic laparoscopic surgery.

About the PhD project within HASTE:  

Within the project, he will work on developing measurements for the early detection of informative data from large-scale spatial and temporal experiments.

Phil Harrison joins the HASTE team to work on predictive modeling with confidence

We welcome Phil Harrison as new PhD Student in the Spjuth lab. Phil obtained his first PhD in marine biology in 2006 studying the population dynamics of grey seals. Between 2006-2016 he undertook several research projects modelling wildlife populations and analysing trends in biodiversity. In the HASTE project, Phil  will develop machine learning methods for online, large-scale analysis of microscopy image data based on statistical earning including e.g. conformal prediction and probabilistic prediction.

Ben Blamey joins the team to work on intelligent cloud services

We are nearing the end of an intensive recruitment period, looking for excellent established and emergent scientists to help us realize the goals of this interdisciplinary project.

This week we are very pleased to welcome Dr. Ben Blamey to the team. He will work in the Hellander lab, in close collaboration with Dr.  Salman Toor, and focus on computer science challenges in designing and developing smart and efficient systems for managing scientific data, and image data in particular, in distributed computing infrastructure such as  hybrid and fog cloud.

With a background on research in machine learning, natural language processing and in development of services in cloud infrastructure both in academia and in industry, Dr. Blamey brings critical experience to the team.

In the featured image Dr. Blamey (right) is busy discussing a potential design of an intelligent system to manage information hierarchies in distributed environments with Dr. Toor (left).

Håkan Wieslander starts a PhD position

We are happy to welcome Håkan Wieslander to the team and to PhD education at the department of Information Technology, Uppsala University!

Håkan grew up in Lund, Sweden and moved to Uppsala 2011 to study Engineering Physics. In 2017 he obtained a masters degree in computational science. The MSc thesis was about classification of malignant cells using deep learning. 

About the PhD project within HASTE:  

Collection of large amounts of data often results in high-quality, highly informative data intermixed with data that is either of poor quality or of little interest in relation to the question at hand. Wieslander’s thesis work will focus on development of computationally inexpensive measurements that will identify non-informative data early on in the analysis process; either online at data collection, or off-line prior to full data analysis. The challenge is to use minimal computational time and power to extract a broad range of informative measurements from spatial-, temporal-, and multi-parametric image data, useful as input for conformal predictions and efficient enough to work well in a streaming setting.