The diffuse interstellar bands (DIBs) are a set of hundreds of mysterious absorption lines that show up in observations of almost any star whose starlight is attenuated by intervening interstellar material -- in any direction in the Milky Way as well as in other galaxies. They are probably caused by an abundant family of large, carbonaceous and stable molecules, but we do not yet know which ones. For this project, you will become a team member of an international consortium (called EDIBLES) to work with the largest and most comprehensive set of DIB observations ever obtained (with the Very Large Telescope). You will use new and modern data processing and possibly machine learning techniques to analyze a subset of these observations to learn more about the properties of the absorbing molecules and the environment in which they reside. This will bring us closer to a solution to the DIB problem.
Research area, student roles & skills
Research area: I study how (carbonaceous) molecules and dust grains form and evolve when dying stars eject their outer layers into space. The chemistry in these environments results in a rich mixture of complex molecules that leave behind unique fingerprints in spectroscopic observations of astronomical objects. By analyzing these fingerprints, we discovered "buckyballs" (C60) in space -- the largest and most stable molecules known in space -- but we do not understand yet how they form. There are hundreds more fingerprints whose chemical origins are unknown, and that represent some of the most stable molecules in the Universe.
Student roles: The student will become part of Western's research group on interstellar matter and of the EDIBLES international collaboration, with access to the entire EDIBLES data set. The student will develop and use software to detect and measure the detailed properties of atomic and molecular lines, and of the DIBs, in a subset of this data set. These properties will then be compared to each other, and to literature data on the physical conditions and chemical abundances in the interstellar clouds. From this, the student will develop quantitative relations between the DIBs and other line of sight properties. Given student interest, the student would also (or alternatively) use chemistry programs to simulate observations of possible DIB carrier candidates and compare these simulations to the astronomical observations. All these studies will bring us one step close to solving the DIB problem. The student will present their progress at the EDIBLES consortium meetings as well as at group meetings.
Skills required: The student should have a good foundation in modern physics (including electromagnetism and quantum physics), a general background or interest in astronomy, and some experience and affinity with computer programming. A good practical knowledge of python and/or experience in collaborative software development (e.g. through github) are a bonus. This position furthermore requires good communication skills, the ability to work in a team in different roles (mentor as well as mentee), and a good work ethic.
The student will investigate quantum optimization techniques applied to a variety of practical problems (resource optimization, time optimization, etc). The project will involve mapping a physical problem to a quantum optimization problem, and then coding it using Qiskit. At the end of the term, the intern will write a report and present a poster at the Windsor Quantum Applications Symposium.
Research area, student roles & skills
Research area: My research is in the theory of atom-field interactions, specifically the control of quantum dynamics using external fields. Of specific interest are techniques of quantum optimization of control fields particularly for achieving more than one objective.
Student roles: The student will explore how a real-life optimization problem can be mapped to a quantum optimization problem, and then try to solve a reduced scale version using Qiskit. Student will participate in weekly group meetings and also present a poster near the end of the term.
Skills required: Familiarity with Qiskit (https://quantum.cloud.ibm.com/learning/en/courses), and optimization methods such as VQE and QAOA.
3. Building Reliable Foundation Models for Astronomical Discovery
COSMIC-FM is a Canadian astronomy-AI initiative developing next-generation foundation-model technologies for scientific discovery in large astronomical archives. Modern observatories are producing unprecedented volumes of imaging, spectroscopic, and time-domain data, creating extraordinary scientific opportunities and significant challenges for analysis that are beyond what current general-purpose AI systems can address directly. COSMIC-FM aims to develop machine-learning methods that can organize, search, and interpret these complex datasets while preserving the scientific information required for reliable discovery.
This Mitacs Globalink project will give the students research experiences at the intersection of artificial intelligence and astrophysics. Working within an interdisciplinary team of astronomers and machine-learning researchers, each student will contribute to a self-contained benchmark study using prepared astronomical datasets and existing workflows.
The project addresses a fundamental challenge in scientific AI: ensuring that machine-learning representations retain subtle but important signals, including faint astronomical sources, rare objects, and weak spectral features. Student projects may include testing the recovery of injected faint sources in images, evaluating the preservation of spectral features under changing noise or resolution, or prototyping simple similarity-search and anomaly-detection tools. By studying model behaviour on real and simulated data, students will help assess the suitability of foundation-model methods for scientific applications and contribute to robust evaluation methodologies.
Reliable foundation models could change how future astronomical archives are explored by helping researchers identify unusual phenomena, accelerate large-survey analysis, give the ability to perform analysis in natural language, and enable new forms of data-driven discovery. Expected outcomes include benchmark analyses, evaluation tools, scientific reports, and open research products that support the long-term development of COSMIC-FM and help establish best practices for applying foundation models to astronomy.
Research area, student roles & skills
Research area: My work bridges astronomy, cosmology, and computational science. For about ten years, I have focused on developing sophisticated deep learning and statistical techniques. These methods are directly applied to interpret the vast and complex datasets generated by new telescopes. Students joining my projects will build practical skills with these advanced computational tools, and directly participate in advancing our knowledge of the universe and the improvement of deep learning methodology.
Student roles: Integral to a research team, the role primarily involves designing and implementing innovative machine learning algorithms. This position extends beyond coding, emphasizing active collaboration with expert scientists to gain valuable insights. A large aspect of the work is dedicated to analyzing and rigorously evaluating the performance of the developed models. This includes testing these models against real-world observational data and sophisticated synthetic physical models, with the exciting potential for contributions to the detection of new astronomical sources.
Skills required: For these challenging projects, strong Python programming ability is essential. Experience with machine learning will give you a significant head start, and a good grasp of statistical analysis is also beneficial. While an interest in astronomy is welcome, it is not a prerequisite. We seek motivated, curious students ready to engage with large, complex datasets and tackle demanding computational problems. Enthusiasm for learning, and difficult problem solving is essential.
4. Design principles of molecular machines
Supervisor: David Sivak
University: Simon Fraser University (Burnaby campus)
Thermodynamics was originally developed to understand steam engines—machines that convert energy into useful work. Today, scientists apply similar principles to microscopic “engines” inside cells, such as molecular motors that power life’s essential processes.
This project investigates how these biological machines achieve remarkable efficiency under noisy, energy-driven conditions. We will study models of key molecular machines, including ATP synthase (which produces the cell’s energy currency), kinesin and dynein (which transport cargo inside cells), and actomyosin (which drives muscle contraction).
Using computational simulations of simple models, you will explore how these systems convert chemical energy into motion and work together as coordinated assemblies. The goal is to identify what makes a molecular machine effective: How do structure, dynamics, and interactions influence performance? What trade-offs exist between speed, accuracy, and energy use?
Your work will contribute to fundamental understanding of how biological systems are crafted by evolution to function reliably under constraints. It may also inform the engineering of synthetic nanoscale machines for applications such as targeted drug delivery, energy harvesting, and biologically inspired computing.
Research area, student roles & skills
Research area: Our interdisciplinary group studies how living systems operate efficiently under physical constraints. We combine statistical physics, molecular biophysics, and information theory to understand how biological processes work reliably despite strong noise and constant energy consumption. By analyzing systems far from equilibrium, we identify fundamental design principles that govern how cells process energy and information. Our research integrates fundamental theory, simple models, numerical computation, and close collaboration with experimentalists to uncover general rules that apply across biological systems.
Student roles: You will develop and study computational models of molecular machines. Your work will involve: * Building and running simulations of simplified biological systems * Analyzing data to understand efficiency, dynamics, and design trade-offs * Connecting simulation results with theoretical predictions * Creating clear visualizations and figures to communicate findings
Alongside exposure to modern research in theoretical biophysics and statistical physics, you will gain practical experience in software development, high-performance computing, data analysis, and scientific communication,
You will work with our research group in a supportive, collaborative environment. You will be closely mentored throughout the project: I meet weekly with each student to provide strategic direction and feedback, and you will also receive day-to-day guidance from a graduate student or postdoctoral researcher.
At the end of the internship, you will present your work to the group. Strong projects may contribute to a scientific publication. The vast majority of my past Globalink interns have gone on to graduate study at leading institutions, including MIT, Cornell, the University of Michigan, LMU Munich, and the University of Alberta.
Skills required: Curiosity, internal drive, and willingness to learn are most important. Helpful background includes: * Introductory thermodynamics and/or statistical physics * Multivariable calculus * Basic programming (Python, MATLAB, or C++) * Familiarity with Linux/command-line tools Experience in biophysics is welcome but not required.
5. Exploration of some fundamental flaws of fluid equations used for modeling non-equilibrium low temperature plasmas
Despite being one of the oldest problems in plasma physics, the interaction between a plasma and a boundary (an electrode or the wall of the reactor) is still an intense area of research due its numerous applications in plasma processes. However, intrinsic difficulties arise in the theoretical description of the plasma boundary because of the need of using simplifying assumptions to describe the non-equilibrium transition between the plasma and the wall. While fluid models are continuity equations (i.e. equations of conservation of mass, momentum and energy) based on an ensemble of independent variables derived from thermodynamics, their validity and use have been extended beyond the cases where the assumption of local thermal equilibrium is valid.
Particle-in-Cell (PIC) codes are the most direct and accurate method to obtain a physically correct description of a system. They do not rely on any assumption concerning local equilibrium and even alleviate completely the need of using a Temperature as variable for describing the energy partition between species. The computational effort required to achieve a steady-state solution, particularly in 2D or 3D is however in most cases daunting. Such models are therefore mostly used for benchmarking fluid or kinetic models which are then used for the optimization of plasma systems.
In this project, a step-by-step approach will be undertaken to develop the most-simple theoretical model possible for describing non-equilibrium plasmas. The goal will be to underline the issues arising from implicit assumptions of fluid models. A system of fluid equations will be defined and solved (analytically or numerically) and directly compared with results from a 1D PIC code [1]. A particular attention will be given to the emergence of multiple (non-physical) solutions arising from the assumptions made by fluid models.
[1] L Schiesko et al 2022 Plasma Sources Sci. Technol. 31 04LT01 https://doi.org/10.1088/1361-6595/ac5eca
Research area, student roles & skills
Research area: I work in the field of plasmas (which are ionized gases and encompass phenomena like lightning, aurora or the fluorescent tube) and their applications in the fields of semi-conductor technology, medicine, agriculture as well as for the green synthesis of chemicals like CO2 neutral fuels. My research focuses on the theoretical and experimental characterization of the non-equilibrium state of plasmas generated in the laboratory. A combination of modelling (fluid and kinetic models) and experimental diagnostics (optical and laser spectroscopies, mass spectrometry,...) are used to characterize the plasma non-equilibrium state and to tune its properties for various plasma applications.
Student roles: The project is open for 2 internships. The two students will first get familiar with non-equilibrium physics and learn the basics of plasma physics, One student will get familiar with an existing Particle-in-Cell code and adapt it for modeling the chosen system. He/She will run simulations and process the data output. A second student will work on the definition of a simplified system of equations to be solved analytically or numerically. Its results will be compared with the output of the one-dimensional PIC code. The two students will work in close cooperation under my daily supervision. The work will be done in collaboration with University of Paris-Saclay and CEA in Cadarache.
Skills required: The student can have a background either in physics, mathematics or engineering and should have a good affinity toward theoretical and/or computational work. No background in plasma science is required while some prior knowledge in non-equilibrium physics and/or statistical physics is a bonus.
6. Extreme Astrophysics: Probing Supernova Physics and Progenitors with the James Webb Space Telescope
Supervisor: Samar Safi-Harb
University: University of Manitoba (Winnipeg campus)
Following successful observing proposals, the PI will be soon acquiring data with the James Webb Space Telescope (JWST) of supernova remnants in order to constrain their physical properties and supernova progenitors.
The project is part of a large international collaboration that will also make use of X-ray data as well as modelling tools in order to interpret the JWST data.
The student will be contributing to the data analysis component of the project, and writing a report at the end of summer.
Research area, student roles & skills
Research area: The research will be in the area of extreme astrophysics, namely the physics of the extreme unattainable on Earth--that of the remnants of explosions of stars that we called "Supernova Remnants". These objects help us probe the physics of explosions, the formation of the heavy elements, the acceleration of high-energy cosmic rays, and the formation of some of the most exotic compact objects such as neutron stars.
Student roles: Research literature, data analysis, coding, and writing a report at the end of summer which will contribute towards the publication of a paper in a refereed journal in Astronomy and Astrophysics.
Skills required: The student will have a Physics or Astronomy background. Programming in Python and writing in LateX are highly desired.
7. From Stars to Space Molecules: Tracing Molecular Formation and Evolution with JWST
The chemistry that leads to the formation and evolution of complex carbonaceous molecules in space is poorly understood. The process begins in the outflows of carbon-rich red giant stars, where relatively simple molecules first form. As these stars evolve through the asymptotic giant branch (AGB), pre-planetary nebula (PPN), and post-AGB phases into planetary nebulae (PNe), infrared observations reveal the emergence of a rich molecular inventory—including large aromatic species such as polycyclic aromatic hydrocarbons (PAHs) and fullerenes (C₆₀, C₇₀). These exceptionally stable molecules are widespread in the Universe and are believed to play central roles in interstellar chemistry and dust evolution. Yet, the physical and chemical mechanisms that drive the transition from small molecules to such molecular complexity remain elusive.
In this project, the student will use spectroscopic data obtained with the James Webb Space Telescope (JWST) for a sample of carbon-rich evolved stars at different evolutionary stages: carbon stars, pre-planetary nebulae, post-AGB stars, and planetary nebulae. The goal is to analyze the molecular emission and absorption features in a quantitative way to identify trends and patterns in molecular formation and destruction. By comparing spectra across evolutionary phases, the student will help constrain the environmental conditions—such as radiation field strength, temperature, and density—that favor molecular growth or lead to molecular breakdown.
Research area, student roles & skills
Research area: I study how (carbonaceous) molecules and dust grains form and evolve when dying stars eject their outer layers into space. The chemistry in these environments results in a rich mixture of complex molecules that leave behind unique fingerprints in spectroscopic observations of astronomical objects. By analyzing these fingerprints, we discovered "buckyballs" (C60) in space -- the largest and most stable molecules known in space -- but we do not understand yet how they form. There are hundreds more fingerprints whose chemical origins are unknown, but that represent some of the most stable molecules in the Universe.
Student roles: The student will become part of the research group on Interstellar Matter at Western University and an integral member of the international consortia responsible for the analysis and dissemination of JWST spectroscopic data. They will work with a subset of our JWST sample. The student will develop and apply software tools to identify and quantify spectral features from PAHs, fullerenes, and related species. Results will be interpreted in the context of the object’s physical environment and evolutionary stage, and compared to those from other sources in our sample or the literature. Depending on the student's interests, the project may also involve radiative transfer modeling, comparison to laboratory or theoretical spectra, or contribution to upcoming publications. The student will present their progress regularly to the research team and may have opportunities to contribute to international collaborative meetings. Through this work, they will gain hands-on experience with cutting-edge astronomical data and contribute to our understanding of molecular complexity in evolved stars.
Skills required: The student should have a good foundation in modern physics (including electromagnetism and quantum physics), a general background or interest in astronomy, and some experience and affinity with computer programming. A good practical knowledge of python and/or experience in collaborative software development (e.g. through github) are a bonus. This position furthermore requires good communication skills, the ability to work in a team in different roles (mentor as well as mentee), and a good work ethic.
The student will participate in research project focused on understanding how galaxies evolve over cosmic time. The project will involve the analysis of large astronomical datasets to investigate the physical processes that regulate star formation in galaxies and drive transitions between active and quiescent states. Possible topics include the impact of dense environments, such as galaxy groups and clusters, on galaxy evolution, as well as the mechanisms responsible for episodes of star formation quenching and rejuvenation. The student will gain experience in data analysis, scientific programming, statistical techniques, and the interpretation of multi-wavelength astronomical observations. The specific project will be tailored to the student's interests and experience while contributing to ongoing research within the group.
Research area, student roles & skills
Research area: My research group uses a combination of observational data and numerical simulations to study how galaxies have evolved over the past ~5 billion years. Our primary focus is understanding the role of environment in driving galaxy evolution. By combining multi-wavelength observations with simulations, we investigate the physical processes that transform galaxies, including changes in their star formation activity, gas content, and morphology, and work to identify the mechanisms responsible for the trends we observe.
Student roles: The student will contribute to an ongoing research project by analyzing astronomical datasets, developing and applying Python-based analysis tools, interpreting results, and participating in regular research group meetings. They will work closely with the supervisor and other group members while developing independent research, data analysis, and scientific communication skills.
Skills required: The ideal candidate will have a background in physics, astronomy, or a related field, and an interest in astrophysical research. Experience with Python programming and data analysis is highly desirable, as the project will involve working with large astronomical datasets. Prior research experience is not required. The most important qualifications are enthusiasm for learning, strong problem-solving skills, attention to detail, and the ability to work both independently and as part of a research team.
9. Holographic Description of Information in String Theory
This project will develop connections between information theory and gravity, especially as described holographically in string theory. Specifically, the AdS/CFT correspondence maps gravitational physics in Anti-de Sitter spacetime, the solution to Einstein's equations with a negative cosmological constant, to the physics of certain quantum field theories (particle physics). This is known as the “holographic” description of the gravitational solution. Recent work has demonstrated how to relate information theoretic quantities, such as measures of information processing, from the particle physics description to the gravity description. In this project, the student will learn key concepts of information theory and how they can appear in different gravitational settings, as well as concepts and calculational skills in general relativity and string theory.
Once the student has built up sufficient background knowledge, the specific calculation or project will depend on the intern’s interests, capabilities and preparation. The emphasis of the project will be to evaluate recent proposals for the gravitational version of some information theoretic quantities in comparison to previous proposals that do not involve as many features of string theory. We will choose some specific black hole or cosmological settings of interest for these calculations.
Research area, student roles & skills
Research area: One of the major developments in quantum gravity (including string theory in particular) of this century is the realization that concepts of information theory provide a useful way to describe gravitational systems. Hawking radiation from black holes provides a central motivation: quantum information cannot be created or destroyed, but a black hole transforms any information that falls past the event horizon into featureless thermal radiation. In fact, an article published in Physical Review Letters in 2016 postulates that black holes actually process information at the maximum possible speed!
Student roles: The student will first carry out a course of study on information theory and its applications in gravity. While details will depend on the student's preparation and interests, the student will then carry out calculations of information theory quantities using gravitational physics in a specific holographic model. There may be opportunity for collaboration with other student(s) in my group.
Skills required: The student should be a physics (or astrophysics) major or else a mathematics, computer science, or engineering major with a strong physics background. A strong background in linear algebra is required, preferably with an understanding of its applications in quantum mechanics. The student will additionally need some experience solving differential equations, and experience with numerical approaches is a bonus. Finally, (formal or informal) exposure to general relativity and/or the relativistic formulation of electromagnetism is very helpful but can be learned during the internship.
10. Multimodality optical imaging and related biomedical applications
There are a few undergraduate research internship positions available in the McMaster Biophotonics Lab. The students will be responsible to develop multimodality optical imaging and sensing systems and explore their biomedical applications. We are looking for aspiring students who have academic background and/or hands-on experiences in either engineering/physical sciences or life sciences.
For students with engineering/physical science background, they will work on a project that develops multimodality imaging/sensing systems which integrate thermal and visible spectral images. The student will 1) design and build prototype imaging systems with commercially sourced cameras of different modalities; 2) build C/C++ based image acquisition software; develop Python/MATLAB based image analysis algorithms.
For students with life science background, they will apply existing multimodality imaging systems to biomedical applications including point-of-care/onsite detection of micro-organisms and disease diagnosis and progressing monitoring. The student will work on 1) develop a cancer cell model for in-vivo imaging; 2) microscopic imaging of live cells; 3) maintaining a biosafety level 2 research facility.
All interns will also perform literature study on topics related to the project; participate in weekly lab meetings and presenting results/progresses; and writing a final report. In these interdisciplinary projects, the intern will learn 1) micro-computer based imaging systems design; 2) image and data processing; 3) basic research methodologies.
Research area, student roles & skills
Research area: Dr. Fang works on the development of optical spectroscopy and imaging systems for biomedical and environmental applications. His recent research projects include: miniaturized optical sensors for water quality monitoring, and optical sensing and imaging technologies for smart home and aging research, optofluidics lab-on-chip point-of-care sensing, optical endoscope designs for gastrointestinal (GI) cancer screening, cancer diagnostics and personalized therapy, fluorescence lifetime imaging microscopy (FLIM) technologies, multimodality imaging for drug discovery and metabolic monitoring.
Student roles: This project is designed to be primarily conducted by the student independently with the help of a senior graduate students. The MITACS student will be responsible for initial design, building the prototype and characterization of its performance through simulated tests.
Skills required: The candidates with engineering/Physical Science background should have strong academic track record in physics, or optics, or electronics. Hands on experiences in data acquisition using microcontrollers are desired but not required. Programming experiences in C/C++ or MATLAB is highly desired.
The candidates with life science background should have strong academic track record in microbiology, molecular biology, biochemistry or related areas. Hands-on experiences in microbiology and/or biochemistry wet lab tasks as well as microscopy are desired but not required. Solid background in statistics are highly desired.
One of the most profound population dynamic shifts in the 21 century is the age structure pyramid being inverted with more 65+ year old than 18 year or younger. As more and more older individuals are living independently in their own homes, many are also living with chronic diseases such as cancer. In response to this reality, McMaster is establishing a new Centre for Emerging Technologies, which will include a Technology Development Park for Healthy Aging. One of the key component of this research infrastructure is a "Smart Home" platform that allows intelligent sensor systems to monitor the activities and environment of the occupants. The proposed research project aim to develop optical sensing methods to track activities and health conditions relevant to older adults living with chronic diseases.
Recent advances in sensors and associated information technologies have led to a number of breakthroughs in remote monitoring of physiological, activity, and other important parameters. For example, multi-axial accelerometers and gyroscopes have been used for monitoring falls and other physical activities; miniaturized electrocardiogram (ECG) and pulse oximeters for heart rate and blood oxygenation; and respiratory rate sensors for breathing patterns changes. Other examples include wireless weight scales, blood pressure cuffs, activity monitoring through non-video based techniques such as optical ranging, and even automated speech and facial express recognition.
Many of these technologies have already led to commercial products, almost exclusively used as personal health monitors instead of patient monitoring. In the case of elderly with chronic conditions, there are critical needs for additional sensor technologies. In this MITACS project, we plan to develop near infrared proximity sensor networks to track the location and activities of the user. An important aspect of the development is integration of information from multiple devices for intelligent information fusion.
Research area, student roles & skills
Research area: Dr. Fang works on the development of optical spectroscopy and imaging systems for biomedical and environmental applications. His recent research projects include: miniaturized optical sensors for water quality monitoring, and optical sensing and imaging technologies for smart home and aging research, multimodality optical biopsy techniques for real-time clinical diagnosis and guided therapy, optical endoscope designs for gastrointestinal (GI) cancer screening, photodynamic therapy (PDT) photosensitizer uptake and dosimetry, fluorescence lifetime imaging microscopy (FLIM) technologies for high content screening.
Student roles: This project is designed to be primarily conducted by the student independently with the help of a senior graduate students. The MITACS student will be responsible for initial design, building the prototype and characterization of its performance through simulated tests.
Skills required: The ideal candidate should have strong academic background in physics, optics, and/or electronics. Hands on experiences in data acquisition using microcontrollers (e.g., embedded systems) are desired but not required. Programming experiences in C/C++ or MATLAB is highly desired.
12. Origin of Species: Dark Matter Production in the Early Universe
This project will focus on dark matter production in the first moments of the universe. Interns will use analytical and numerical tools (Python) to compute the number density of particles produced by an epoch of cosmic inflation, and identify parameter regimes that match the observed density of dark matter. The students will then compute the imprint of this in the cosmic microwave background, in the form of isocurvature perturbations. A team of 4 students will collaboratively compute the isocurvature perturbations in 4 models of dark matter: spin-0, spin-1/2, spin-1, and spin-3/2.
Research area, student roles & skills
Research area: My research area is particle cosmology. Trained as a formal theorist (including string theory and supergravity), my work now combines theory, numerics (coding), and data, to construct and constrain models of dark matter and the production of dark matter in the first moments of the universe. My recent dark matter work has on "WIMPZillas" -- a hypothetical particle that is so heavy that it could only have been produced in the first moments of the universe. Other work in my group includes primordial black holes, dark energy, and connections with string theory.
Student roles: The student will receive a crash course in theoretical cosmology, including both fundamental quantum aspects and data-oriented observational aspects. The intern will then study the fluctuations of the universe on cosmological scales generated by particle production happening on microscopic scales. This will involve analytical and numerical solutions to differential equations.
Skills required: The student should have studied quantum mechanics and should have some basic familiarity with Python coding. Other topics that are useful but not required are quantum field theory, cosmology, and general relativity.
The student will be part of the BESPOKE / PUGS (MUGS3) collaboration galaxies project, which is producing the most realistic models to date of specific nearby galaxies with billions of stars, gas and dark matter. These simulations incorporate treatments for gravity, fluid dynamics, radiation and other physics so that the simulated galaxies can evolve and naturally reproduce the properties of the real galaxies they are modeled after. The simulations use millions of CPU hours on super computers to model billions of years of simulated galaxy evolution. The results are directly comparable to recent and upcoming observations from the James Webb Space Telescope (JWST), the Atacama Large Millimeter Array (ALMA, e.g. PHANGS) and other optical and radio telescopes that allow us to probe stars and gas under in all phase (plasma, neutral and molecular). These now have unprecedented resolution down to the scale of individual star forming regions in giant molecular clouds. The goal of the project is to produce close simulated matches to nearby galaxies including several aspects simultaneously: star formation rates and properties of star clusters, stellar and dark matter dynamics, gas distribution and phases, molecular cloud properties, turbulence and magnetic fields. Individual nearby real galaxies have tight relations in these properties with a much smaller spread than the galaxy population as a whole and thus provide a stringent test of our understanding of galaxies and how these relation fundamentally arise from the properties specific to each galaxy (e.g. its pre-existing stellar disk and bulge, dark matter halo and gas supply). The project has several participants including faculty, postdocs and graduate students both at McMaster University and internationally.
Research area, student roles & skills
Research area: My research focuses on creating realistic galaxies, giant molecular gas clouds, stars and planets with super-computer simulations that explicitly model the fluid dynamics, gravity and other physics at high resolution. Galaxies contain billons of stars and produce more every year. We want to discover how young stars' strong ultraviolet light, supernovae explosions and other physical interactions push gas around to regulate star formation in galaxies. We also want to study how this changes over the age of the universe as galaxies evolve. We also explore the roles of dark matter, magnetic fields and galaxy collisions.
Student roles: The student will work with simulated galaxy data to explore how well the simulated galaxies match observed ones and which physical processes are critical to getting a good match. The exact role of the student will depend on their interests and capabilities in consultation with the supervisor. The student will read in super-computer simulation data using Python scripts and produce analysis, visualizations and/or movies. This may include mock observations that can be compared to telescope data. Mock observations let us better understand limitations on observations and systematics (e.g. resolution, beam smearing, noise) . They will have opportunities to interact with expert observers (faculty, postdocs and students) at McMaster and compare to state-of-the art observational data (e.g. ALMA, PHANGS). The student can focus on specific aspects such as the molecular cloud population and extract a simulated cloud catalogue to compare with observations of actual galaxies. The student will interact with group members to explore ways to compare simulations and data and understand the role of small-scale physics (e.g. star formation, stellar populations) and the computer model for them (e.g. depending on the student interest in physical modelling and programming). This can include comparing simulations with different physical assumptions (e.g. magnetic fields strengths, turbulence models, star formation and explosive feedback treatments, dark matter halo mass and distribution) to understand their effects. The student can participate in designing new simulations if interested. The student will have an office with other students working on similar projects and be a full member of the McMaster astronomy group to participate in discussion, seminars and other activities. They will daily access to mentoring and help from other group members.
Skills required: The student should have a strong physics background (can also be astrophysics or applied mathematics with physics courses). The student should have demonstrated interest in astronomy/astrophysics and programming. This would ideally include upper year courses in mathematical physics, astronomy/astrophysics and programming. The student must have experience in Python programming through research work and/or coursework. Experience with Unix and C/C++ (compiled programming languages) would be helpful. The student should be able to write and debug Python scripts mostly independently (with help from local mentors and supervisor).
14. Quantitative Susceptibility Imaging in the Spinal Cord
This internship project will contribute to the development of quantitative susceptibility mapping (QSM) for the spinal cord. QSM is an advanced MRI technique that can provide information about tissue composition, including features related to myelin and iron, but most existing methods have been developed for the brain and do not transfer easily to the spinal cord. The spinal cord is much smaller, more affected by motion, and more sensitive to magnetic field inhomogeneities, which makes both image acquisition and reconstruction especially challenging.
The student will work on one or more aspects of this problem, depending on their background and interests. Possible directions include MRI sequence development, image reconstruction and processing, simulations, digital phantom studies, optimization of QSM pipelines, and evaluation of image quality and quantitative measures in healthy participants or neurological disease. The project may also involve developing methods to better account for the specific anatomy and microstructure of the spinal cord, including white matter organization and susceptibility anisotropy.
This position is well suited for a student interested in MRI physics, medical imaging, signal processing, computational modeling, or neuroimaging methods. The student will join a research environment focused on advanced quantitative MRI and will contribute to methodological work with potential applications to neurological disorders such as multiple sclerosis.
Research area, student roles & skills
Research area: My research focuses on developing advanced magnetic resonance imaging (MRI) methods to better characterize tissue microstructure and composition, with a particular emphasis on the spinal cord and brain. I work on quantitative MRI techniques, including quantitative susceptibility mapping (QSM), as well as methods for improving image quality, reconstruction, and biophysical interpretation. My research combines MRI physics, image processing, and computational modeling, with applications in neurological disorders such as multiple sclerosis and in understanding normal tissue development and aging.
Student roles: The student will contribute to an ongoing research project focused on developing and evaluating MRI methods for quantitative susceptibility mapping (QSM) in the spinal cord. Their role will depend on their background and interests, but may include helping with MRI data organization and analysis, testing image-processing pipelines, running simulations, reviewing scientific literature, and assisting with the interpretation and presentation of results.
The student will work closely with the research team and receive training in the relevant concepts and methods. They will be expected to participate actively in the project, learn new tools, ask questions, and gradually take ownership of specific tasks. Depending on the project direction, the student may also contribute to code development, quantitative analysis, figure preparation, or experimental planning.
This role is intended as a learning experience for an undergraduate student who is interested in medical imaging research. The goal is for the student to gain hands-on experience in a collaborative research environment while contributing to the development of advanced MRI methods for the spinal cord.
Skills required: The student should have a background in engineering, physics, biomedical engineering, neuroscience, computer science, or a related field. Prior experience in MRI, programming, image processing, or data analysis is an asset, but not required. The intern will have the opportunity to learn those skills during the internship.
The most important qualities are curiosity, motivation, and a strong interest in learning about MRI and medical imaging research. The student should be interested in scientific problem-solving and comfortable working in a research environment.
The research project will involve learning how to code quantum algorithms using python based programs like qiskit or circ. The project will also introduce the student to the IBM quantum composer, and teach them how to run a program on a real quantum computer. Currently there are several algorithms which find quantum computer solutions to differential equations. This work mainly concerns quantum gate based codes of numerical algorithms for solving non-linear differential equations. This might involve matrix inversion as in HHL or eigensolver code as in Variational quantum algorithm. One could have a Hamiltonian simulation as well. Quantum algorithms for the non-linear systems are being investigated and it is an interesting field of research. The inherent probabilistic nature of quantum systems and their use in codes, might give us solutions beyond the digital system. At the theoretical level, the project will try to find more efficient ways of coding the non-linear system, and simultaneously explore new solutions to the complex system, particularly search for long time behaviour. The aim would be to simulate a complex system like the weather or climate and make predictions using mathematical models such as Lorenz, or Lorenz-84 systems. It is well known that quantum computers using quantum states can process vast amount of information, and therefore they are the technology of the future for studying climate models. The student will begin with an introduction to quantum mechanics, quantum algorithms, the IBM system and qiskit. Then a basic non-linear system like the Lorenz system will be coded, and we will run it on the IBM quantum computer to see the results. Monte-Carlo methods of describing statistical systems will also be introduced, and a climate model will be simulated using the quantum computer. A publication based on the results is expected at the end of the internship.
Research area, student roles & skills
Research area: Quantum Computers promise a more efficient and faster algorithm for some of our current computational uses. The quantum computation algorithms are based on gate based circuits. In this project we will be finding new quantum algorithms which have use for differential equations in mathematics. In particular our project will investigate the quantum algorithms for solving non-linear differential equations which describe complex systems like weather and climate change. The systems like the Lorenz and the Lorenz 84 system have been solved classically, so our quantum solutions will also be analyzed for differences with the classical solutions.
Student roles: The student will be initially taught the basics of quantum coding. This involves learning qiskit on python, and simulating basic quantum codes like solving 2X2 matrix equations. They will then run the program on a real quantum computer to verify the code. There is a group working in quantum algorithms at University of Lethbridge, and the student will learn from the peers, seniors, with guidance form the supervisor. After the initial learning period, the student will work with a team which is already engaged towards developing algorithms for some pre-assigned differential equation. The group will have regular meetings where the results obtained will be discussed and the student will make presentations at these meetings to report on the progress. The daily work will be recorded as codes in the local computer provided, and or in online repository such as github/IBM. When a particular differential equation has been solved and the results obtained, a publication will be written. This will also be a team production, and the student will be assigned specific sections to complete and then submit to the supervisor. It is expected that the student will report twice weekly the progress in coding and seek advise on the next step of the project. The student is also expected to acquire understanding of the physics of the system the non-linear differential equations describe. As the overall aim of the project is to investigate complex systems and their application in simulating climate change , it is expected that the student will read about the purpose of the quantum computation code. In summary the student will function in a team of quantum coders, and learn this new innovative technology which will eventually take up commercial space. They will also learn about the physics of complex systems and learn to simulate climate models.
Skills required: The student should be proficient in Linear Algebra and basics of coding. There should be some familiarity with python which is used for quantum algorithms. The student should also know quantum mechanics, and understand what a quantum state or a qubit is mathematically. Matrix algebra is the main mathematical technique which is used in quantum computation. Quantum states and quantum circuits are also described using Dirac notation of the abstract vector space. Knowledge of non-linear differential equations will be an asset. Knowledge of numerical methods like the Euler method, the Runge-Kutta methods for solving differential equations are expected.
Inspired by a rare plant discovered in Central Asia that buries its seeds in soil and sand using wind-induced vibrations, we investigate a novel mechanism by which a vibrating, textured surface can generate propulsion in granular media such as sand and powder. To uncover the fundamental physics and explore potential applications, we have developed both simulations and experimental platforms. Remarkably, our simulations reveal that this mechanism functions not only under Earth’s gravity (1g) but also in microgravity (0g)—an unexpected and exciting result. In collaboration with the Canadian Space Agency (CSA) and the European Space Agency (ESA), we are now working toward experimental validation of our microgravity simulations through parabolic flights and upcoming missions on the International Space Station (ISS). We also want to build some applications based on this physical mechanism: examples include i) a micro robot that can move in sand, soil, grains, for planetary exploration and monitoring in agriculture, ii) powder-based high precision 3D printing, and iii) powder-based heat management system for extreme environments (ultra-high/low pressure and temperature). All these applications are expected to be deployed in Earth gravity and reduced gravity (e.g., Moon, Mars, and orbital).
Research area, student roles & skills
Research area: Interdisciplinary research at the crossroads of soft matter physics (especially fluid mechanics and granular matter), data science, engineering, with a splash of art and humor.
Student roles: The students will assist the graduate students in our group to design, build, and conduct the experiments and/or simulations. Literature search and data analysis are also expected.
Skills required: This is a large project; any skills or background, as long as solid, would be helpful Background: physics, chemical engineering, mechanical engineering; Skills: experiments, manufacturing, data processing, high-speed imaging, coding
17. Stellar Kinematics of a Candidate Supermassive Black Hole Binary Host Galaxy
Supervisor: John Ruan
University: Bishop's University (Sherbrooke campus)
Binary systems of supermassive black holes are natural outcome of hierarchical galaxy formation, but no such systems have been found. These binary systems lose angular momentum and eventually merger due to gravitational wave emission, and their low-frequency gravitational waves should be detectable by ongoing pulsar timing array experiments. One popular approach to identifying candidate supermassive black hole binaries is to search for radial velocity shifts of broad emission lines in their spectra, akin to radial velocity searches for exoplanets. Recently, multi-epoch spectra of the quasar J0950+5128 obtained over 20 years have been shown to display these shifts (see arXiv:2505.06221); this is our current best candidate for a supermassive black hole binary identified through this approach. To corroborate these claims, my group has obtained and reduced integral field spectroscopy of the host galaxy of J0950+5128, using the Gemini-North telescope. In this project, the student will use software to fit the resultant spectral datacube, and measure stellar kinematic properties of the host galaxy. If J0950+5128 indeed contains a supermassive black hole binary, we expect the stellar kinematics to be complex, with slow rotation (see arXiv:2504.21145).
Research area, student roles & skills
Research area: I work on electromagnetic counterparts to gravitational wave sources, across both the electromagnetic spectrum and the gravitational wave spectrum. In other words, I use multi-wavelength telescope observations to study binary neutron star mergers detected by the Laser Interferometer Gravitational wave Observatory (LIGO), supermassive black hole binaries at the centres of massive galaxies that will be detected by pulsar timing array experiments, and massive black hole mergers in smaller galaxies that will be detected by the Laser Interferometer Space Antenna (LISA) mission.
Student roles: The student will lead this project. Since the integral field spectra has already been reduced, the remaining analysis should be able to be completed within the 12 week internship. The student will undertake this project in the Astrophysics Lab at Bishop's, which houses offices shared by several graduate students and undergraduates doing research in astrophysics.
Skills required: The student should be proficient in data analysis in Python, including writing scripts to read and fit data, regression, and plotting. Previous experience in fitting spectra (especially of galaxies) is ideal, although not necessary.
18. Topics in observational exoplanetary astronomy and stellar astrophysics
There are opportunities for interested GRI students to contribute to a variety of research projects, including but not limited to studying the demographics of planets around M dwarfs,
precise measurements of exoplanets’ physical and orbital parameters, and the connection of planetary parameters to their host stars. The specific project will depend on the interests of the candidate and on what open problems would benefit the most from a high-quality research student.
Research area, student roles & skills
Research area: The study of extrasolar planets is a rapidly growing field of astrophysics that has seen the discovery of more than 6300 exoplanets in only a few decades, the development of dedicated space and ground-based observatories to study the exoplanet population, and the active search for life beyond our solar system. Our research group focuses on observational studies of the formation and evolutionary processes of exoplanets that orbit the most common stars in our galaxy, low-mass M dwarf stars.
Student roles: 1) Read the papers provided by the supervisor to build your background knowledge in the specific field of study. 2) Attend weekly meetings with the supervisor throughout the duration of the project to monitor progress and address any issues that arise. 3) Lead the research project by writing code to analyze and visualize data, maintain research notes on your progress and questions that arise, and contribute to writing a research paper based on your methods and results (if applicable). 4) Participate in weekly research group meetings and journal clubs with other exoplanet astronomers to strengthen your background knowledge in various topics related to stars and exoplanets, and to expand your network of collaborators at McMaster. 5) Give an oral presentation at the end of your GRI term to either the exoplanet research group and/or as part of McMaster's Summer Student Symposium, typically held in early August.
Skills required: I am looking for an enthusiastic GRI student with a background in physics and astronomy, or a related field. Experience with Python — particularly with package installation, data analysis, and visualization — would be a strong asset. As importantly, I require that the successful applicant have a willingness to learn and to interact with our group of exoplanet astronomers.