4x Postdoctoral Positions AI in Computational Medicine at the University of Manchester
Alejandro Frangi
🌟 Looking for a career where you can blend ML, computer science, maths, physics, statistics, and cardiovascular science?
Want to work in a top 50 University and the home of Alan Turing? 🧬🤖
🔬 Dive into high-fidelity models, craft virtual patient cohorts & revolutionise in-silico trials.
Your PhD skills in computational mechanics, computational imaging and deep learning will pave the way for breakthroughs!
🚀 Positions open:
2 x Research Associate in Multiphysics-Informed ML for Cardiovascular Modelling
We are seeking to appoint two ambitious and proactive Research Associates to be part of a multidisciplinary team developing high-fidelity models of cardiovascular fluid dynamics and device-flow/device-tissue interactions through multi-physics and physiological modelling. The candidates will leverage clinical and experimental data, first-principle physics-based simulations, scientific machine-learning approaches, data assimilation, and uncertainty quantification techniques. Integral to this work will also be the application of these techniques to large real-world multimodal datasets, clinical trials datasets and population imaging studies.
Applicants should have a PhD in computational cardiovascular mechanics and prosthetic valves or related fields and expertise in either computational solid mechanics to analyse soft-tissue deformations and device interactions or computational fluid mechanics to enable analysis of haemodynamics and thrombosis. One of the critical challenges we want to tackle is how to efficiently execute ensembles of virtual experiments entailing. Experience in working with multiphysics and multiscale models and in accelerated methods for solving partial differential equations and scientific machine learning (physics-informed machine learning) is essential.
🔗 www.jobs.manchester.ac.uk/Job/JobDetail?JobId=28032
2 x Research Associate in Multimodal Foundation Models & Generative AI
We are seeking two ambitious and proactive Research Associates to be part of a multidisciplinary team, focusing on image-based multiphysics modelling of cardiovascular fluid dynamics and device-tissue interactions. The successful candidate will utilise clinical and experimental data to pioneer novel gen-erative AI and geometric deep learning approaches to create synthetic virtual patient cohorts from multimodal data. This role involves developing advanced algorithms and high-throughput workflows for crafting virtual populations and simulation-ready computational anatomy models, integrating tissue microstructure properties where relevant. The role requires applying innovative techniques to large, real-world multimodal datasets, including clinical trials and population imaging studies.
Applicants should have a PhD (or nearing completion) in computational imaging and deep learning, and an understanding of applied mathematics, focusing on algorithm design and analysis. Proficiency in modern ML techniques, including geometric deep learning, diffusion models, and neural networks for multimodal image analysis will be essential, as well as expertise in Python and C/C++ for scientific computing, and in ML/DL frameworks like TensorFlow, PyTorch, Keras, and Scikit-learn.
🔗 www.jobs.manchester.ac.uk/Job/JobDetail?JobId=28073
💡 You bring:
- PhD (or about to complete it)
- Expertise in computational fluid/solid mechanics or imaging & ML
- Python & C/C++ prowess
- A passion for innovation!
✨ We offer:
- Leading pension scheme
- Supportive health & wellbeing services
- Generous holidays + Christmas closure
- Flexible, hybrid working
🌈 Join a team that values diversity & drives scientific excellence!
⏰ Hurry, applications close at midnight on the closing date! Check the links for person specification criteria. 📅