dr hab. inż. JAROSŁAW PAWŁOWSKI

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Adres e-mail jaroslaw.pawlowski@pwr.edu.pl
Dyscypliny naukowe

Physical sciences
Strona domowa https://github.com/jarek-pawlowski
Profile naukowe

Google Scholar

deep learning in physics course

Tematyki badawcze

Tematyka badań:
Zastosowanie zaawansowanego uczenia maszynowego w fizyce
Platformy do obliczeń kwantowych w ciele stałym
Adaptacyjne protokoły tomografii kwantowej
Dynamika złożonych płynów (odd fluids, viscoelasticity, turbulence)

Obszary badawcze:
Machine learning in (quantum) physics
Physics-informed neural networks
Geometric (symmetry-preserving) deep learning
Quantum computing platforms
2D materials
Many-body physics
Complex fluids
Computational physics

Potencjalne tematy doktoratu:
Quantum machine learning in the NISQ (Noisy Intermediate-Scale Quantum) era
Physics-informed neural networks for Hamiltonian learning (quantum nanostructures, materials discovery)
Learning efficient quantum tomography protocols
Symmetry-preserving (gauge-equivariant) deep neural networks

Jeżeli jesteś zainteresowany/a, zajrzyj tutaj:
https://jarek-pawlowski.github.io/MLphys/
Jeżeli interesują Cie przedstawione tam metody i chciał(a)byś je zastosować do różnych zagadnień fizyki, to zapraszam!
Słowa kluczowe

Machine learning in (quantum) physics, physics-informed neural networks, geometric (symmetry-preserving) deep learning, quantum computing platforms, 2D materials, many-body physics, complex fluids

computational physics
Email address jaroslaw.pawlowski@pwr.edu.pl
Scientific disciplines

Physical sciences
Personal website https://github.com/jarek-pawlowski
Research profiles

Google Scholar

deep learning in physics course

Research topics

Research topics:
Application of advanced machine learning in physics
Quantum computing platforms in solid-state systems
Adaptive quantum tomography protocols
Dynamics of complex fluids (odd fluids, viscoelasticity, turbulence)

Research areas:
Machine learning in (quantum) physics
Physics-informed neural networks
Geometric (symmetry-preserving) deep learning
Quantum computing platforms
2D materials
Many-body physics
Complex fluids
Computational physics

Potential PhD topics:
Quantum machine learning in the NISQ (Noisy Intermediate-Scale Quantum) era
Physics-informed neural networks for Hamiltonian learning (quantum nanostructures, materials discovery)
Learning efficient quantum tomography protocols
Symmetry-preserving (gauge-equivariant) deep neural networks

If you are interested, take a look here:
https://jarek-pawlowski.github.io/MLphys/
If the topics presented there interest you and you would like to apply them to various problems in physics, feel free to get in touch!
Keywords

Machine learning in (quantum) physics, physics-informed neural networks, geometric (symmetry-preserving) deep learning, quantum computing platforms, 2D materials, many-body physics, complex fluids

computational physics