Physical and Materials Engineering
The curriculum concentrates on the comprehensive study of materials properties and failure processes from the point of view of physics and physical metallurgy. Students should develop capability to apply their knowledge in inventive manner to new technologies and materials, such as plasma spraying, special methods of thermo-mechanical and thermo-chemical treatment, etc. Special attention is paid to the degradation processes and to the synergetic effects of various materials properties on material failure. The subjects of study are metallic and non-metallic materials, e.g., structural ceramics, polymers, amorphous and nanocrystalline materials and intermetallics.
The Ph.D. programme requires proficiency in mathematics and physics at the MSc. degree level obtained from Faculty of Science or Faculty of Mechanical Engineering.
Issued topics of Doctoral Study Program
- Complex media imaging exploiting machine learning approaches
In modern experimental set-ups, the use of holographic techniques is already a common standard; whether they are experiments using optical tweezers or advanced imaging techniques. This technology opens up the possibility of new applications not only in research in physics, but also in the fields of biology, medicine and others. A similar technological revolution is taking place in the field of computer science, especially thanks to a considerable step forward in the computational capabilities of modern parallelizable and distributed platforms. This makes it possible to perform complex structure calculations in virtually real time, either using traditional "white-box" models based on the numerical solution of equations or, increasingly popular, using "black-box" models of machine learning / artificial intelligence. This area has shown considerable progress over the last decade and diverse applicability across disciplines. To mention a few of the applications in the field of filtering and separation of 1D and 2D data, expert systems or so-called reinforcement learning, where the machine learns to control another machine without a teacher, only using feedback from the system. This makes it an attractive candidate for solving a wide range of problems in complex photonics applications, and can be used throughout the experimental system from hologram design optimization, through filtrating of measurement data, expert and assistance systems to augmentation of experiments on real-time measurement evaluation.
Course structure diagram with ECTS credits
Study plan wasn't generated yet for this year.