We are happy to present our newest member of HASTE: Dan Rosén! Dan is joining the group of Ola Spjuth to work as a Data Engineer. In his projects he will work with data pipelines and interact closely with microscopes to help reaching the goals of HASTE to act on collected image streams and make intelligent decisions and control microscopes to prioritize collecting the most interesting data.
Data Engineer position available in HASTE
We are looking for a skilled Data Engineer to join the HASTE team!
In collaboration with other researchers, develop, implement and test systems for AI-controlled automated microscopes. The task includes interacting directly with the microscope and establishing pipelines where models trained on previously taken images decide and control where the microscope should take images in the next step to reach a specific goal. We are looking for a candidate with a genuine interest in technology and automation, and who enjoys solving problems including both practical interaction with hardware (robots, microscopes) and different types of software. Since our microscopes generate large amounts of images, the position will also include large-scale data management and -analysis. You will work with researchers in AI modeling and biological laboratory sciences, and contribute to implementing methods and evaluating them for different types of biological problems.
This is a 2-year position that is part of the HASTE project, funded by the Swedish Foundation for Strategic Research (SSF) aiming at developing new, intelligent ways of processing and managing very large amounts of microscopy images in order to be able to leverage the imminent explosion of image data from modern experimental setups in the biosciences. Industry collaborators are Vironova AB and AstraZeneca AB.
A master’s degree in engineering or a university degree in a relevant field is a requirement. Good programming skills in Python and preferably more programming languages is a requirement. Experience in AI modeling, Linux systems as well as developing REST services and APIs is a requirement. Experience of AI modeling on image data, practical handling of automated microscopes and working with software containers (e.g. Docker/Singularity) is meriting.
Apply via link at the bottom of the University application: https://uu.se/en/about-uu/join-us/details/?positionId=361223
Deadline: Nov 25th, 2020
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: https://www.uu.se/en/about-uu/join-us/details/?positionId=327845 (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.
PhD position in Scientific Computing
We’re recruiting a PhD student in Scientific Computing in the area of smart data stream management. For more information visit our recruitment page.
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.