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.