The HASTE project is coming to an end by June 2023 and a final meeting was held on 2023-05-26 at the Ångström Laboratory, Uppsala. The participants were: Ola Spjuth (UU), Phil Harrison (UU), Ankit Gupta (UU), Ebba Bergman (UU), Andreina Francisco (UU), Tianru Zhang (UU), Carolina Wählby (UU), Salman Toor (UU), Alan Sabirsh (AstraZeneca), Andreas Hellander (UU), Ida-Maria Sintorn (Vironova).
The meeting program featured presentations reflecting on the HASTE project as a whole, but also presentations by PhD students on the completed projects and a more visionary future perspectives on the continuation of HASTE. One example is the new grants received by the HASTE PIs to carry on with parts of the projects, including an eSSENCE funded PhD project on streaming data analysis of microscopy imaging (PI: Ola Spjuth) and the award from UU Invest’s Venture Challenge 2023 (Ankit Gupta, Ida-Maria sintorn and Carolina Wählby) to continue the SimSearch application(see Gupta et al. IEEE J. Biomedical and Health informatics 26(8), 4079-4089, 2022).
It has been a great project with many successful scientific outcomes. The list of publications summarizes the scientific papers, but also highly important are the code contributions in GitHub and the datasets deposited as a result of the project.
During 2023, we look forward to two HASTE PhD students to defend their theses [updated]:
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.
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.
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
Discovering new drugs is becoming more costly. Lars Carlsson gave a presentation Machine Learning For Smarter Drug Discovery at RISE SICS Data Science & AI Day, Nov 28, 2017, where he gave some examples of how AstraZeneca is trying to improve the drug discovery phases through the use of machine learning.
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.