University of Cambridge

Applications are invited for a Bioinformatician in the laboratory of Professor Steve Jackson based in the internationally renowned Gurdon Institute in the centre of Cambridge, UK (https://www.stevejacksonlab.org/).We wish to recruit a versatile individual to participate in data-driven projects addressing mechanisms by which mutations arise and how they influence cancer cell proliferation and survival. The post-holder will work on collaborative projects based on biological data generated by other group members. There will also be opportunities to lead specific project areas, in line with the overall research objectives of the lab.

01/02/2024
Full time
University of Cambridge Cambridge, UK
Applications are invited for a postdoctoral researcher to work on efficient machine-learning systems for earth observation. The post holder will be part of the Computer Architecture group under the guidance of Prof. Robert Mullins. The role involves developing new low-power systems and algorithms to significantly enhance the capabilities of onboard AI when used within small satellites. The project is a collaborative effort involving teams at the University of Manchester and University of Southampton and partners at the Alan Turing Institute and HMGCC. The research will seek innovations at both the software and hardware level. The use of a novel memory system and recent advances in low-power machine-learning accelerators will create significant scope to explore new research directions. On-board processing tasks will include object detection, compression, interpretation and various image retrieval tasks. The "Perfect Recollection for Clearer Insight" project is...
19/01/2024
Full time
University of Cambridge Cambridge, UK
This exciting project will focus on addressing two fundamental challenges in physics-enhanced machine learning strategies in applied mechanics: (i) Overcoming poor generalisation performance and physically inconsistent or implausible predictions of machine learning models in applied mechanics by developing approaches integrating physics (first principles) knowledge through biases within Machine Learning (ML) algorithms to inform physics (e,g. identification of unknown constitutive laws and nonlinearities from measurements and physics-knowledge). (ii) Identification of incorrect prior physics assumption (e.g. wrong constitutive model) in the physics-enhanced machine learning algorithm. During this project you will carry out dynamic tests on a laboratory setup, process data and develop advanced physics-enhanced machine learning techniques to identify nonlinearity under sparse noisy data and wrong physics-biases. You will present the outcomes of your work at...