Vacancy for dissertation/PhD project *Learning Search and Decision Mechanisms in Medical Diagnosis* We are looking for a new PhD student for a fully funded interdisciplinary research project investigating the computational mechanisms underlying the perceptual processes in medical imaging. *Brief project description and goals* Medical experts routinely screen images for signs of abnormality indicative of a disease. Experimental evidence shows that these experts, based on their training, are able to make a diagnose above chance level even after screening images for only a few seconds. Such visual inspection utilizes global ensemble scene statistics, or “scene gist”, to provide contextual guidance information. The goal of the PhD project is to investigate how such global, contextual gist information can be computed and incorporated in machine vision approaches, such as deep neural networks for natural or medical image processing. In particular, a goal is to develop neural mechanisms that can be integrated into existing pre-trained CNNs to compute scene gist for rapid global decision-making and contextual guidance in medical images. Such mechanisms should explain how such global feature compositions are learned to increase the classification specificity and, at the same time, do not equip an observer with the ability to precisely localize such feature compositions indicative of the evidence. At the implementation level, computational units (neurons) in hierarchical CNN architectures will be extended by integrating local bottom-up feature extraction mechanisms with top-down modulatory contextual fields. We focus on these mechanisms to learn the integration of information streams in counter-stream networks by employing novel two-point information integration units inspired by recent findings from neuroscience. These investigations will contribute to develop novel CNN architectures by integrating feedforward and feedback data streams that operate at different spatio-temporal scales and feature types inspired by computational mechanisms in biological vision. The combined global and local information is expected to provide more detailed explanations on a mechanistic level how radiologists steer their attention and search capacities and learn to improve such skills. *Requirements* - MSc degree in Computer Science, Engineering, Physics, Mathematics, Cognitive Systems/Science or equivalent relevant background - Experience in computational vision, neural networks, machine learning; with strong mathematical/physics foundation - Experience in developing biologically inspired models – or interested in learning how to develop such mechanisms and models - Experience in analysis of large data and evaluation of results - Strong coding skills - Personal skills: working in a team, but should be able to work independently, is strongly focused and enthusiastic about the project theme, is self-motivated and takes responsibility for reaching milestones - Experience and talent in scientific writing (with proficient written & oral English) *Contact & application* The project is part of the newly founded research training group “KEMAI” that focuses on integrating knowledge and learning based approaches for better medical AI (https://kemai.uni-ulm.de/ for more information). The offered PhD position is fully funded (E13 salary) and also benefits from an interdisciplinary setting, the ability to connect with peers from related projects and a clearly structured PhD program. Application materials are expected until November 18, 2024. They should contain a CV, statement of research interest and background in relation to the KEMAI training group and project theme C2. A cover letter should inform about the expected date of availability and name of two referees. Submit your application via the KEMAI website (preferred) or to Prof. Heiko Neumann (heiko.neumann@uni-ulm.de) or reach out for more information. Best regards, Daniel Schmid -- Daniel Schmid, M.Sc. Institute of Neural Information Processing Faculty of Engineering, Computer Science and Psychology Ulm University James-Frank-Ring D-89081 Ulm Germany e-mail: daniel-1.schmid@uni-ulm.de