2 PHD positions in Mathematics, Theoretical Phyiscs, Control Systems, Theoretical Machines
Two Phd Positions are available in the Systems Control group of the University of Luxembourg, in the framework of the Doctoral Training Unit CriTiCS on Critical Transitions in Complex Systems.
PhD position #1 (theoretical): Classification and detection of critical transitions.
PhD position #2 (applied): Early detection of heart attacks and atrial fibrillations.
See project description below for position #1, see companion advert for position #2.
Supervisor: Prof. Jorge Goncalves.
Start Date: flexible from now until September 2018.
Closing date for applications: open until filled.
Funding: full funding available for up to 4 years, with a highly competitive salary.
- Hold (or being about to obtain) a Master degree in Mathematics, Theoretical Physics, Control Systems, Theoretical Machine Learning or related fields.
- Strong mathematical background is a requirement.
- We will only consider students that graduate in their top 20% undergraduate and Master's class rank (equivalent to a UK first class degree).
- Excellent communication and interpersonal skills, enthusiasm and great commitment to research.
- Excellent working knowledge of English.
To apply and for further information: www.critics.uni.lu.
Informal inquires: Dr. Stefano Magni, email@example.com.
These positions are inserted in the framework of the interdisciplinary Doctoral Training Unit CriTiCS which encompasses 11 PhD positions and confronts the topic of critical transitions in complex systems within a range of disciplines including the areas of physics, clinical science, biology and finance. CriTiCS is a project based at the Luxembourg Center for Systems Biomedicine.
The University of Luxembourg is an equal opportunity employer. All applications will be treated in the strictest confidence.
Phd position #1 (theoretical): Classification and detection of critical transitions
The goal of this project is o provide a classification of critical transitions between alternative stable states of non-linear dynamical systems, and associated early warning signals for diverse applications. The success of detecting early warning signals has varied so far with the particular field. However, most cases have not taken into account the actual dynamics that generated the data: different forms of dynamical behaviors can lead to very different ways a critical transition occurs. For instance, a critical transition can occur as a bistable systems switch from one stable (desired) equilibrium to the other (undesired). In this case, the system contains both states and the switch can be triggered by external effects (such as, in the case of systems of biomedical relevance, stress or alcohol). Or a system may have a single state, and changes in a single parameter can cause bifurcations and transform the system into multiple equilibria. The key to anticipating critical transitions lies in understanding what kind of system we have. Hence, the first step is to build a classification tool, initially using simplified low-dimensional analytical models to gain a deep understanding of critical transitions, then generalizing to more realistic and high-dimensional systems. For each class of critical transitions, we will investigate which combination of signals optimally amplifies the detection of the proximity to a tipping point. Then it will become possible to classify real-world applications.
Link to apply : http://emea3.mrted.ly/1i4m8
This job comes from a partnership with Science Magazine and