The Computer Science department is looking for a PhD candidate to work within the H2020 CompBioMed project described below.
The topic of the work is to create and implement a numerical model for the transport of red blood cells and platelets by the plasma. These elements will be described by deformable suspensions in a Newtonian fluid. Simulation will be performed to better understand pathologies and interaction between the blood and vessel walls. The modeling framework will be the Lattice Boltzmann method, using high performance computations.
The candidate should have:
- A Master in computational science, physics, applied math or computer science
- Skills in scientific programming (C++ and MPI are desirable)
- Skills in modeling and simulation (knowledge of the
Lattice Boltzmann method and Palabos solver would be very welcome) - Knowledge in biophysics would be an advantage
- Good knowledge of written and spoken English
Candidates should send their CV and motivation letter to Bastien.Chopard@unige.ch and Jonas.Latt@unige.ch
Expected start of the project: Oct 1st, 2016.
The CompBioMed project aims at estabilishing a Centre of Excellence that will advance the role of computationally based modelling and simulation within biomedicine. Three related user communities lie at the heart of the project: academic, industrial and clinical researchers who all wish to build, develop and extend such capabilities in line with the increasing power of high performance computers. Three distinct exemplar research areas will be pursued: cardiovascular, molecularly-based and neuro-musculoskeletal medicine.
Predictive computational biomedicine involves applications that are comprised of multiple components, arranged as far as possible into automated workflows in which data is taken, from an individual patient, processed, and combined into a model which produces predicted health outcomes. Many of the models are multiscale, requiring the coupling of two or more high performance codes. Computational biomedicine holds out the prospect of predicting the effect of
personalised medical treatments and interventions ahead of carrying them out, with all the ensuing benefits.