Thesis topics in Physics

Application deadline: 14th December 2024.

  1. Insights into the Reaction Mechanism in Superheavy Element Synthesis within Multidimensional Dissipative Dynamics
    • Supervisor: dr hab. Michał Kowal, prof. NCBJ
    • Auxilliary Supervisor: dr Y. Jaganathen
    • Description: This research will explore reaction mechanisms in cold and hot fusion reactions leading to superheavy nuclei, using six-dimensional dissipative dynamics besed on Langevin’s microscopic formalism. We will examine entrance channel effects, such as spin distribution and angular momentum in heavy ion collisions, with an emphasis on the “hindrance mechanism” in superheavy element production. Time characteristics of key reaction processes will also be thoroughly analyzed, offering a deeper understanding of fusion dynamics.
    • Funding: NCBJ
  2. Determination of the hadronic vacuum polarization contribution to the muon anomalous magnetic moment
    • Supervisor: prof. dr hab. Wojciech Wiślicki
    • Co-Supervisor: prof. dr hab. Andrzej Kupść
    • Description: The muon anomalous magnetic moment g-2, where g is the gyromagnetic ratio or the g-factor of the muon, is one of the exceptional cases in particle physics at low energy where a significant discrepancy between measurement and Standard Model (SM) prediction has persisted for more than 20 years. This discrepancy, now reaching 4.2 sigma, could be a compelling evidence of physics beyond the SM. In view of the new experimental efforts, underway at Fermilab (USA) and J-PARC (Japan) to improve the g-2 accuracy, a confirmation of its SM prediction is now mandatory. Its hadronic contributions are under particular scrutiny, as they induce the main uncertainty of the SM prediction. The KLOE experiment at the phi-factory DAphNE in Frascati (near Rome) is the first to have employed Initial State Radiation (ISR) to precisely determine the e+e- –> pi+pi-gamma cross section below 1 GeV.
      This PhD project is focused on the analysis of the full KLOE data sample and should improve by a factor 2 current accuracy on contribution to g-2 from hadronic vacuum polarization. It will place a significant constraint on the SM prediction to the muon g-2. The project requires involvement in development of Monte Carlo generator for this process. A successful candidate is expected to work on the analysis of data and simulation software, and will be assisted by experienced senior colleagues.
    • Funding: NCBJ
  3. Low surface brightness galaxies in the cosmic web
    • Supervisor: prof. dr hab. Agnieszka Pollo
    • Auxilliary Supervisor: dr Anna Durkalec
    • Description: The upcoming Legacy Survey of Space and Time (LSST) will be the benchmark for the next decade, providing unprecedented depth and quality optical data on millions of galaxies, including low and high surface brightness galaxies (LSBGs and HSBGs, respectively). This PhD project focuses on the analysis of the identified LBGs, i.e. galaxies that are very diffuse and fainter than the typical night sky, and their location in the cosmic web, using the LSST data. The main goal of the project will be to study the relations between LSBGs and the Large Scale Structure of the Universe aiming to understand how the LSBGs are linked with the underlying dark matter field. Different approaches will be used, including clustering analysis (correlation functions, cross-correlations, marked statistics), as well as local density measurements. This approach can be then extended towards the theoretical interpretation of the results in the context of different cosmological and dark matter models.
      This PhD project will be conducted as part of the NCN MAESTRO project “Barely Visible: Low Surface Brightness Universe in the LSST era,” led by Prof. Agnieszka Pollo.
      Our team is an active member of the LSST collaboration and will have access to early data releases at the beginning of 2027. In our team we have experts in LSBGs, galaxy morphology, SED fitting, machine learning, and galaxy clustering. Therefore, this PhD project offers a unique opportunity to gain expertise in all these aspects. The results of this research will provide essential insights into the nature of very faint galaxies and their role in galaxy formation and evolution scenarios.
    • Funding: NCN
  4. Mechanical Properties of Metallic Glasses
    • Supervisor: prof dr hab. Mikko Alava
    • Auxilliary Supervisor: dr Silvia Bonfanti
    • Description: This PhD project focuses on metallic glasses, a class of materials with an amorphous atomic structure that imparts high strength and elasticity. Metallic glasses are of particular interest in condensed matter physics due to their unique structure-property relationships, which differ from conventional crystalline metals. This research will examine how compositional variations and cooling rates influence the mechanical properties of metallic glasses, aiming to identify underlying principles that govern these behaviors.
      The project will employ a combination of theoretical modeling, experimental techniques, and machine learning approaches. Machine learning methods will be applied to analyze large datasets, uncover patterns in material behavior, and develop predictive models. The candidate’s work will contribute to a deeper understanding of metallic glass behavior, providing insights relevant for both fundamental physics and potential practical applications.
    • Funding: NCN
  5. Machine Learning-driven modeling of atomic dynamics in high entropy materials
    • Supervisor: prof dr hab. Mikko Alava
    • Auxilliary Supervisor: dr Silvia Bonfanti
    • Description: This PhD project investigates the atomic-scale dynamics and structural organization in both amorphous glasses and high entropy alloys (HEAs), focusing on the mechanisms underlying glass formation, structural stability, and relaxation in these complex materials. Amorphous glasses lack long-range atomic order, while high entropy alloys consist of multiple principal elements in nearly equal proportions, resulting in unique structural configurations and enhanced stability. Both material types exhibit complex behaviors due to their atomic disorder, especially around transitions, such as the glass transition in glasses and phase stability in HEAs. The project will explore how variations in atomic bonding, local density, and structural motifs impact key physical properties, including viscosity, elasticity, thermal conductivity, and resistance to deformation. Extensive molecular dynamics simulations will be used to model atomic interactions, local rearrangements, and transition states within these systems. Machine learning techniques, including neural networks and unsupervised learning algorithms, will be applied to analyze large datasets from simulations, identifying patterns and correlations within atomic configurations that influence material properties.
      The primary goals of this project are to (1) develop machine learning models capable of predicting the dynamic responses of glasses and HEAs based on atomic structures, (2) identify structural motifs associated with specific behaviors, and (3) improve theoretical understanding of phase stability, relaxation mechanisms, and aging in disordered systems. This research aims to create predictive models for both glassy and high entropy materials, providing insights for designing materials with tailored, stable amorphous or high-entropy states suitable for advanced applications.
    • Funding: NCN
  6. Additive Manufacturing of Metallic Alloys
    • Supervisor: prof dr hab. Mikko Alava
    • Auxilliary Supervisor: dr Silvia Bonfanti
    • Description: This PhD project centers on the study of metallic glasses in the context of additive manufacturing (3D printing). Metallic glasses possess an amorphous atomic structure that contributes to high strength and elasticity, properties that are increasingly sought after for various high-performance applications. This research aims to investigate the feasibility and mechanical outcomes of 3D printing these materials, examining how the printing parameters and post-processing techniques affect their structural integrity and overall mechanical properties.
      The project will combine experimental approaches with computational modeling to address challenges specific to 3D printing of metallic glasses, such as avoiding crystallization and optimizing printing conditions. Additionally, machine learning techniques may be utilized to analyze data patterns and support the development of predictive models for optimizing printing parameters. This research has implications for advancing the use of metallic glasses in engineering and applied physics.
    • Funding: FNP
  7. Proton charge-radius measurement using muon-proton elastic scattering
    • Supervisor: prof dr hab. Krzysztof Kurek
    • Co-Supervisor: dr hab. Marcin Stolarski
    • Description: The size of the proton is correlated with attempts to explain the confinement of quarks and gluons. Today there is a puzzle over that size; namely electron-proton scattering experiments and laser measurements of Lamb shift in muonic atoms are in a significant disagreement. This discrepancy may point to physics beyond the Standard Model or it could mean that low-Q2 scattering is more subtle than previously thought. In either case, solving the puzzle is crucial and the new measurements, preferably with new techniques, are of utmost priority. Such a new technique will be applied in the AMBER experiment using high-energy low-Q2 elastic muon-proton scattering at the M2 beam line of the CERN SPS starting in the year 2025. A high-precision measurement at low-Q2 realised with a high-pressure hydrogen time-projection chamber (TPC) can contribute to a solution of the puzzle, especially in view of different systematics of this approach compared to electron scattering.
    • Funding: NCBJ

Application deadline: 5th August 2024.

  1. Gluon Saturation in Quantum Chromodynamics at high energy
    • Supervisor: dr hab. Tolga Altinoluk, prof. NCBJ
    • Description: One of the most appealing questions in physics that has been studied for several decades is the fundamental structure of matter in Nature. The vast amount of effort devoted to understand the basic ingredients of matter and their interactions resulted in the theory known as Quantum Chromodynamics (QCD). This theory describes the interactions between the elementary strongly-interacting particles known as quarks and gluons. These elementary particles combine and form composite particles known as hadrons, such as protons and neutrons.
      QCD is at the focus of both experimental and theoretical studies for many decades with the aim of understanding the structure of hadrons. Due to the complexity of the theory, a full understanding of the structure of the hadrons has not been achieved and it is still under investigation. One of the key features of QCD is that the number of gluons inside a hadron increases very rapidly with increasing energy. This phenomenon is related with the fundamental property of gluons which tend to split into the daughter gluons. However, one central question is whether this increase can continue unboundedly. Continuous theoretical efforts over the last three decades lead to a phenomenon known as gluon saturation which can be explained as follows. At sufficiently high energies, another property of gluons become important. In a dense environment, gluons start to recombine and this process slows down the increase of gluon density. This brings a nonlinear aspect to the dynamics of the interaction of the elementary particles. Studying the gluon saturation phenomenon and its nonlinear nature is very important to understand the structure of hadrons.
      One of the biggest colliders that studies the fundamental structure of matter is the Large Hadron Collider at CERN in Switzerland. This collider has provided a vast amount of data to study structure of hadrons. Moreover, a new collider (Electron-Ion Collider) will be built in the USA which will provide a clean environment to study the fundamental structure of matter within the QCD framework. Significant improvement in the theoretical calculation framework is needed in order to fully benefit from the existing data and to provide theoretical support for future phenomenological studies. Therefore, the main goal of this PhD project is to develop new theoretical tools that will improve our understanding of gluon saturation and will shed light on the fundamental structure of matter.
    • Funding: NCN
  2. Gravitational lensing of the cosmic microwave background in the era of big surveys
    • Supervisor: dr hab. Paweł Bielewicz, prof. NCBJ
    • Co-Supervisor: prof. dr hab. Marek Biesiada
    • Description: Gravitational lensing of the cosmic microwave background (CMB) is a relativistic effect caused by the gravitational interaction of the CMB photons with matter inhomogeneities encountered during their travel from the last scattering surface to an observer. Reconstructed from correlated CMB ansiotropy gravitational potential of the lensing structures projected along the line-of-sight gives a unique image of the formation of the large scale structure at high redshifts and enables testing cosmological models at large scales. On the other hand, generated by the lensing effect divergence-free component of CMB polarisation has to be precisely estimated and corrected to be able to detect primordial gravitational waves produced during the inflationary epoch.
      This PhD project will involve research on different aspects of the CMB gravitational lensing effect including developing and implementation of algorithms for estimation of the gravitational lensing potential, cross-correlations with galaxy and radio surveys, correcting CMB maps for the lensing effect and constraining amplitude of the primordial gravitational waves and sum of neutrino masses. The PhD student will analyse publicly available data from the Planck satellite and other ongoing and near future CMB surveys. The cross-correlation studies will be realized within the framework of the Rubin Observatory Legacy Survey of Space and Time survey. We seek strongly motivated PhD candidates with interest in cosmology who have experience in programming and numerical methods.
    • Funding: NCBJ
  3. A comprehensive approach to the studies of gallium oxide implanted with rare earth ions for future optoelectronic device applications
    • Supervisor: prof. dr hab. Elżbieta Guziewicz
    • Co-Supervisor: dr hab. Renata Ratajczak
    • Description: Gallium Oxide (Ga2O3) is one of the currently most popular materials, that has attracted great attention from the scientific community. According to our project plan, this material’s properties will be modified by doping with Rare Earth (RE) using ion implantation. Such Ga2O3:RE systems could be essential for future applications in optoelectronics. In your work, you will deal with the structural, electrical, and optical research of a range of physical phenomena related to the ion implantation process, as well as designing the parameters to obtain efficient monochromatic light source emitters based on this material. An extremely important part of your studies is going to be the development of the Ga2O3 technology using the Atomic Layer Deposition (ALD) growth method. For the structural characterization of RE implanted Ga2O3, both bulk single crystals and ALD layers, mainly the RBS/c analytical technique, and comparative methods like Raman and XRD analysis will be used. You are going to be involved in the PL and Hall effect measurements too. Most of these investigations will be realized outside NCBJ, mainly at the Institute of Physics PAS, Warsaw, Poland. If you join us, you will also have a great opportunity to perform experimental investigations in many other European research centers as well as present results at international conferences. We are looking for candidates with basic knowledge of structural or optical studies of monocrystals. Experience in layer growing with the ALD technique will be well-received.
    • Funding: NCN
  4. Corners in classical and quantum gravity
    • Supervisor: prof dr hab. Jerzy Kowalski-Glikman
    • Description: In recent years, a new line of research in classical and quantum gravity has emerged based on the study of finite regions. There are new nontrivial observables associated with the boundaries of the regions. It has been shown that these boundary charges satisfy a universal algebra which is independent of the details of the dynamics in the bulk. The universality of the corner algebra has led to the claim that it could play a role in classifying states of quantum gravity, similar to that of Poincare symmetry in quantum field theory. There are also fascinating ramifications of corner charges and algebra in the field of quantum gravity phenomenology. The investigations of the PhD student will be part of the activities of the newly approved COST Action CA23130 “Bridging high and low energies in search of quantum gravity”.
    • Funding: NCBJ
  5. Impact of radiation damage on mechanical and structural properties of martensitic ferritic steels
    • Supervisor: dr hab. Łukasz Kurpaska, prof. NCBJ
    • Auxilliary Supervisor: dr T. Khvan
    • Description: Materials under neutron irradiation are subject to irradiation damage and degradation of their mechanical properties. However, neutron irradiation studies for research purposes are complicated, time-consuming, and expensive, so emulating neutron irradiation damage by much faster and cost- effective ion irradiation is of high interest. At the same time, ion irradiation avoids activation of the irradiated material. However, the emulation of neutron irradiation by ion irradiation damage is limited by transferability issues and experimental uncertainties linked to the limited penetration of the energetic ions.
      The goal of the work is to improve our understanding of the phenomena associated with the formation and evolution of ion irradiation-induced defects and their role in the deformation behavior in ferritic/martensitic (f/m) steels, incl. pure Fe, Fe9%Cr, Fe9%Cr-NiSiP and Eurofer 97. Nanoindentation will be carried out using different indenter shapes to study irradiation hardening. Radiation-induced defects will be identified by TEM. Complementary analyses will be carried out, including plasma-focused ion beam tomography of indents.
      The project aims at establishing and validating a comprehensive testing protocol with complementary investigations to support the experimental activities. An experimental/computational procedure is targeted for effectively characterizing ion irradiation as a surrogate for neutron irradiation. The project will also contribute to developing and validating models for size effects during nanoindentation in irradiated steels through a statistical sampling approach across a specimen surface. It is well known that size effects are influenced by irradiation; however, the origin has been debated. Micromechanical models and validation experiments will be performed that will account for geometrically necessary dislocations generated at the indent. Based on dislocation dynamics formulations in samples with various defects/defect clusters, a machine-learning approach will be developed to evaluate nanoindentation responses in irradiated metals.
      The Ph.D. project is expected to effectively improve experimental/computational protocols for the fast, safe, and reliable assessment of the irradiation impact on the mechanical properties of structural steels. The topic is in line with the research carried out by the NCBJ and in line with the mission of the JRC. Therefore, the Ph.D. candidate will conduct his/her research for up to 24 months at the Joint Research Centre in Petten under employment as a grant holder through the Collaborative Doctoral Partnerships (CDP) scheme (Agreement n. 36149). The candidate will be enrolled in NCBJ Ph.D. school and awarded a Ph.D. Degree in Physics at the end of the three to four-year Programme after successfully completing the entire Ph.D. requirements, including the successful defense of the Thesis as provided by the NCBJ Ph.D. school Regulations on Ph.D. Programmes.
    • Funding: NCN
  6. Linking dust attenuation and radio data
    • Supervisor: dr hab. Katarzyna Małek, prof. NCBJ
    • Auxilliary Supervisor: dr Pratik Dabhade
    • Description: In the era of large, precise radio observatories such as LOFAR or SKA, and large optical surveys such as LSST, combining optical and radio data seems to be the best way to overcome the sometimes unavailability of infrared data. Although radio and infrared wavelengths probe different physical properties of galaxies, they still provide important information about the star formation rate of galaxies and hence the dust attenuation.
      This project aims to combine deep radio and deep optical surveys to link proxies for dust attenuation, and hence stellar mass and star formation rate properties, of normal star-forming galaxies, but also of the low surface brightness galaxies that will be discovered in large numbers in the LSST era. The work will start with data from the North Ecliptic Pole field, where the low surface brightness galaxies have already been selected from the LSST-like deep optical data, and which is covered by the LOFAR deep field.
    • Funding: NCBJ
  7. Reliably identifying merging galaxies in large surveys
    • Supervisor: dr hab. Katarzyna Małek, prof. NCBJ
    • Auxiliary Supervisor: dr William Pearson
    • Description: With ongoing and upcoming large surveys, such as the Legacy Survey of Space and Time (LSST), that will observe billions of galaxies, we need quick, efficient, and reproducible methods to identify different types of galaxies, including galaxy mergers. We currently have a number of different methods to identify galaxy mergers, such as visual identification, morphological statistics and machine learning. However, we do not have a clear picture of which method is the best. These methods are also not easily transferable between different surveys.
      In this PhD project, the successful applicant will compare existing merger identification methods. The student will compare machine learning methods with the more classical morphological parameter selection. This will show, for the first time, which of these methods is the most reliable. The PhD student will also develop their own methods to identify galaxy mergers, which can consistently identify mergers in different survey data. This project will use state of the art observations from the Hyper Suprime-Cam and Euclid as well as data from the latest large cosmological simulations. As our team is an active member of LSST, the PhD student will be perfectly placed to apply their methods to create the first merger identifications for LSST.
      Our team is one of the leading global research groups in galaxy mergers. We also have wide ranging expertise in morphological classifications and machine learning. The team also has a deep understanding of the physics that galaxies in general, and galaxy mergers in specific. As a result, this PhD project offers a unique opportunity for the student to gain expertise in all of these areas.
      With highly accessible techniques that will be developed, modern technologies, and modern and near- future data sets, the results of this PhD project will be highly impactful and will influence future galaxy merger research.
    • Funding: NCN
  8. Properties of low surface brightness galaxies in the LSST era
    • Supervisor: dr hab. Katarzyna Małek, prof. NCBJ
    • Co-Supervisor: prof. dr hab. Agnieszka Pollo
    • Description: The upcoming Legacy Survey of Space and Time (LSST) will be the benchmark for the next decade, providing unprecedented depth and quality optical data on millions of galaxies, including low and high surface brightness galaxies, LSBs and HSBs, respectively. This PhD project focuses on the identification and analysis of LSBs, which are very diffuse and fainter than the typical night sky, using the LSST data. The PhD student will investigate intermediate galaxies between LSBs and HSBs to understand if there is an intrinsic separation or continuity between the two populations.
      In the framework of this project, the PhD candidate will perform morphological analysis on these galaxies to estimate their properties such as size, surface brightness, and concentration. Several widely used tools like Galfit, Photutils, Autoprof, and machine learning techniques will be used to compare and evaluate the robustness of different tools for optimal morphological estimation of faint galaxies. The student will also carry out a multi-wavelength analysis of these galaxies by compiling ancillary data from the literature (e.g., GALEX, Spitzer, JWST, Herschel). The CIGALE tool will be used to perform Spectral Energy Distribution (SED) fitting techniques to estimate the stellar mass, star-formation rate, and dust attenuation of the galaxies.
      Our team is an active member of the LSST collaboration with access to early data releases that will be available at the beginning of the 2024 year. We also have experts in the field of LSBs, galaxy morphology, as well as SED fitting. Therefore, this PhD project offers a unique opportunity for the student to gain expertise in all these aspects. The results of this research will provide essential insights into the nature of very faint galaxies and their role in galaxy formation and evolution scenarios.
    • Funding: NCN
  9. Low surface brightness galaxies in the LSST era as a Big Data challenge
    • Supervisor: prof. dr hab. Agnieszka Pollo
    • Auxiliary Supervisor: dr Junais
    • Description: The upcoming Legacy Survey of Space and Time (LSST) will be the benchmark for the next decade, providing unprecedented depth and quality optical data on millions of galaxies, including low and high surface brightness galaxies (LSBGs and HSBGs, respectively). This PhD project focuses on the identification and classification of LSBGs, i.e. galaxies that are very diffuse and fainter than the typical night sky, using the LSST data. The successful PhD candidate will develop and apply methods (in particular, but not only, methods based on different machine-learning-approaches) to identify LSBGs in optical data, starting from the existing survey data, with the aim to apply the developed methodologies to the coming LSST survey. The next task will be to classify so-obtained catalogs of LSBGs into sub- populations and analyze the properties of these populations with the aim of understanding the physical reasons behind their diversity.
      This PhD project will be conducted as part of the NCN MAESTRO project “Barely Visible: Low Surface Brightness Universe in the LSST era,” led by Prof. Agnieszka Pollo.
      Our team is an active member of the LSST collaboration and will have access to early data releases at the beginning of 2027. We have experts in LSBGs, galaxy morphology, SED fitting, and machine learning. Therefore, this PhD project offers a unique opportunity to gain expertise in all these aspects. The results of this research will provide essential insights into the nature of very faint galaxies and their role in galaxy formation and evolution scenarios.
      A successful candidate is expected to be already skilled in programming, with a particular emphasis on machine learning applications.
    • Funding: NCN
  10. Neutrino reconstruction via their acoustic detection using advanced intelligence computational methods at KM3NeT experiment
    • Supervisor: dr hab. Artur Ukleja
    • Description: Neutrinos are certainly not what typically comes to mind when hearing the word astronomy. Nevertheless, together with the gravitational waves they are gaining importance in studies of the cosmos today. They allow probing otherwise impenetrably dense regions, while retaining directionality, since they are not deflected by the magnetic fields.
      Detecting them has been a challenge, successfully tackled in the previous century. Now, we are entering an era of multimessenger astronomy, including High Energy Physics, with neutrinos and precision measurements of their properties. Both areas are covered by the network of underwater Cherenkov neutrino telescopes currently constructed at two sites in the Mediterranean Sea by the KM3NeT collaboration.
      This topic is focused on identification of neutrinos via acoustic signals generated during their interactions with matter. To classify neutrino types, the advanced intelligence computational methods will be used as GBDT, graph neural networks, sophisticated transformers and unsupervised techniques.
      The PhD candidate should ideally have some prior experience with particle physics (HEP) or/and Astro particle physics, programming (we prefer python but it could be the other modern language), data analysis and computational intelligence methods with deep learning algorithms. The candidate should also have familiarity with statistical tools as we will calculate the probability of a neutrino belonging to a given class.
    • Funding: NCBJ

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