Massive black holes (>1e6 Msun) are expected to reside at the centres of most massive galaxies in the Universe, and a broad range of observational evidence supports a close connection between these objects and the properties of their host galaxies, such as stellar mass, luminosity, and morphology. These correlations point toward a co-evolution scenario in which galaxies and their central black holes grow together over cosmic time. Despite this general framework, a population of overmassive black holes has been identified that challenges standard evolutionary models. Their rapid assembly, particularly at high redshift, is still not well understood and represents an important open question in models of black hole and galaxy formation. Galaxy mergers add further complexity to this picture. When two galaxies interact and merge, their central massive black holes are expected to sink toward the centre of the remnant through dynamical processes, eventually forming a gravitationally bound system known as a massive black hole binary (MBHB). These binaries are among the most promising sources for current and future gravitational-wave observatories, including Pulsar Timing Arrays (PTAs) and the Laser Interferometer Space Antenna (LISA). In addition to their gravitational-wave emission, massive black hole binaries may also produce electromagnetic signatures, making them key targets for multimessenger astronomy. The combined detection of gravitational and electromagnetic signals would provide a unique window into the physics of black hole mergers and their surrounding environments.

In this work, we aim to investigate the diversity of single massive black holes, explore the formation pathways of massive black hole binaries, and assess their detectability through electromagnetic signatures. To do so, we employ a combination of semi-analytical models and cosmological hydrodynamical simulations, which together offer complementary perspectives on the growth and evolution of black holes in a cosmological context.

Spanish contribution -the Science Diagnostics Subsystem- to LISA, the future space-borne gravitational wave detector with expected launch in 2035. A particular interesting challenge arising in LISA and other fundamental physics space missions is the high stability control of temperature in the very low-frequency range (below the milliHertz). Our group is currently developing the techniques with potential impact in these future missions. For that purpose we are investigating temperature sensing by means of phase locking to optomechanical resonators. 

In our lab, a temperature sensor in the nano-Kelvin precision regime is being developed using optical systems, for its applications in Gravitational wave detection. The main element is a Whispering Gallery Mode (WGM) resonator which will serve the purpose of an optical cavity that provides time stability in the low frequency domain. The resonator itself can be used as a temperature sensor.

The Pound-Drever-Hall technique (PDH) is used to stabilize the Laser (which is the source of light for the resonator) in frequency, using the direct outputs of the Cavity (WGM resonator). To implement this technique, a frequency modulated beam is locked to a cavity. The output of this beam is measured and down mixed with an oscillator (in phase with the frequency modulation of the beam) to obtain the “off resonance” of the laser with respect to the cavity. This way a feedback loop can be implemented to correct the laser frequency towards resonance with the cavity, in our case, the WGM resonator.

The candidate work will consist of the development, characterization and optimization of the feedback control loop of the PDH technique, and apply it to an existing optical setup working in ultra-stable regime. The objective is the characterization of the PDH lock at low frequencies and the optimization of the parameters of the experiment to achieve nano-Kelvin performance down to the millihertz measuring bandwidth.

The Gravitational Astronomy group at the Institute of Space Sciences (ICE-CSIC) is leading the Spanish contribution -the Science Diagnostics Subsystem- to LISA, the future space-borne gravitational wave detector with expected launch in 2035. Our group is also leading the design of an In-Orbit Demonstrator (IOD) selected to fly in the forthcoming cubesat of the Generalitat de Catalunya New Space strategy (expected launch 2026).

The objective of ILIADA (In-orbit LISA diagnostics demonstrator) is to place in orbit novel technologies that are being developed for the detection of gravitational waves in space and to test them in a LEO environment with the aim, also, to put to test noise characterisation and subtraction algorithms. ILIADA will consist of high-precision temperature sensors, magnetometers and a radiation monitor that will represent a first version of the future LISA diagnostics system.

The magnetometers signal on-board ILIADA will be dominated by the space-craft and the Earth magnetic field contribution. The candidate work, in collaboration with the ILIADA multidisciplinary team, will be to develop data analysis methods that allow the subtraction of these contributions. The aim of the work is to enable ILIADA to detect Earth magnetic field features, as the so-called Birkeland currents. These methods are expected to have an impact in the future LISA instrument characterisation pipelines.

LISA (Laser Interferometer Space Antenna) will be the first gravitational‑wave observatory in space. The mission requires an extremely stable in‑flight environment to detect signals at millihertz frequencies. To guarantee this stability, the Science Diagnostics Subsystem monitors temperature, magnetic fields, and radiation around the gravitational reference sensors and optical benches. Its correct operation is essential both for interpreting the scientific data and for ensuring that environmental fluctuations remain well below mission requirements.

This project focuses on the on‑ground validation of the Elegant Bread Board Model (EBBM) of the Diagnostics Subsystem. The EBBM integrates the Data Acquisition Unit (DACU), together with representative temperature sensors, magnetic sensors, and the radiation monitor. The student will participate in the full chain of functional and performance verification, working alongside the ICE‑CSIC team responsible for Spain’s contribution to LISA diagnostics.

The training will include hands‑on work with laboratory instrumentation, execution of measurement procedures, and acquisition of sensor data under controlled thermal, magnetic, and radiation conditions. The student will help characterize sensor sensitivity, bandwidth, and low‑frequency stability, evaluate the end‑to‑end performance of the DACU, and compare measurements with mission requirements and existing simulations. They will also contribute to data processing using Python tools commonly applied in space‑instrumentation testing.

This project offers an excellent introduction to precision instrumentation for space science, providing direct involvement in a subsystem with a key role in ESA’s flagship gravitational‑wave mission. The student will gain laboratory experience, data‑analysis skills, and insight into the workflow of space‑qualification activities within an international collaboration.

Asteroids are usually covered by fine grained, collisionally processed regolith, changing the reflectance properties of these challenging bodies. We will work with chondritic meteorites: thick sections and grinded materials as the best proxies to simulate asteroid surfaces.

The experiments will be taken using a Brucker VIS-NIR spectrometer under vacuum at the ICE-CSIC Meteorite and Sample Return clean laboratory, where the reflectance
spectra will be taken in different modes and spectral windows. We will use these laboratory spectra to compare with the spectra of real asteroids, identifying from
the absorption bands and features the main rock-forming minerals and processes at work in interplanetary space.

Microquasars are binary systems composed of a compact object and its stellar companion. The compact object, either a black hole or a neutron star, is constantly accreting plasma from the star -- forming a relativistic jet that can extend up to several parsecs. Interestingly, due to the presence of internal shocks or through its interaction with the interstellar medium, this jet can act as a powerful astrophysical particle accelerator. As the origin of Galactic cosmic rays is unknown, that could imply that a significant portion of cosmic rays originate in microquasars, a hypothesis supported by recent gamma-ray observations. But then, a population large enough should exist. Where are they? And how many do exist? The objective of this project is to identify this new population of particle accelerators using gamma-ray observatories.

Participating in this project, you will learn how to analyse real gamma-ray data from the Large Area Telescope on board the Fermi satellite (Fermi-LAT) using maximum-likelihood, time series, and Monte Carlo statistical methods. In particular, you will identify the best candidates according to multi-wavelength data and search for gamma-ray emission caused by cosmic-ray bubbles around microquasars, or gamma-ray flashes during powerful flares of their jets. The results will be contextualised with one-zone hadronic and leptonic models of the most plausible emitting regions within and around microquasars. Note: following the student's preference, an alternative project of a similar nature could also be applied to supernovae.

We propose a range of topics based on forecasting and determining the capabilities of AtLAST using maria (https://github.com/thomaswmorris/maria), ranging from exploring nearby objects like the Galaxy, Magellanic Clouds, external galaxies both nearby and far into the early universe, and the overall large scale structure of the universe, including the cosmic microwave background radiation from the Big Bang itself. We also propose developing fundamental technical capabilities for the telescope, such as instrumental properties, polarization, and spectroscopy. And finally, we propose development of data analysis and handling methods like machine learning through Gaussian processes (nifty denoising; see https://github.com/jwuerzinger/CMB_denoising), maximum likelihood mapmaking to process polarization and 3D spectral data cubes, and pipeline development to process the massive amounts of data that AtLAST and its precursors will generate. If any of the above areas interest and excite you, we are open to explore the possibilities and converge on an interesting subset of the many topics outlined above.

Ultra-diffuse galaxies provide a unique opportunity to study the dark matter puzzle. Their extraordinarily low surface brightness allows us to probe the distribution of globular clusters all the way to their centre, and thus to probe the entire population. Using archival data from the Hubble Space Telescope, this work aims to study the number of globular clusters in several ultra-diffuse galaxies. These galaxies have been the source of much recent controversy, as their formation challenges current models of galaxy formation. The study of their globular cluster systems, coupled with very deep imaging, allows us to better understand the true origin of these peculiar galaxies.

The student will learn the techniques for making globular cluster counts and exploring the diffuse light of these faint systems. They will work with one of the leading teams in this field. At the end of this TFM, the student will be able to participate in the scientific debate on this hot topic.

Stellar halos extend far beyond a galaxy's brightest regions. These regions contain a record of all the merging events that a galaxy has experienced. Therefore, it is important to characterise the properties of these halos in order to understand how galaxies assemble. However, these regions have low star densities and are therefore very challenging to observe. This is why we know very little about the properties of this diffuse component in galaxies.

In this Master's thesis, the student will use cosmological simulations to learn how to measure this component. They will measure light profiles, colours, and the quantity of light in order to gain a full understanding of stellar halos and enable us to interpret observations.

Characterization of exoplanets requires high instrumental precision as well as combining measurements from very different sources in a common framework. In addition to this, numerous instrumental and astrophysical degeneracies exist so interpreting this high quality data requires holistic and unbiased techniques to combine all the data consistently. Although we can simulate most of the complexity of the observations, these degeneracies are often difficult to predict a priori, creating all sorts of false negative and false positive detections of exoplanet features.

To account for this, and to accommodate the increasing complexity of astronomical datasets, we will implement data and model driven machine learning techniques. Instead of predicting all the cross dependencies a priori, we will use deep neural networks to identify exoplanetary features in time-series (true Doppler signals, true transit signals) and on spectroscopic observations such as the ones that are being obtained from ground based and space based observatories. This project will work with both synthetic and real observations from ground and space based observatories. Detailed knowledge of machine learning is not required, but good coding skills (especially in Python, which is the main coding language for machine learning techniques) are strongly recommended.

In the last ten years, hundreds of exoplanets have been detected using blind surveys around nearby, bright, low-mass M-type stars with high-precision spectrographs like HIRES, HARPS, HARPS-N, or CARMENES.

The detection of such exoplanets always depended on a variety of parameters which have evolved and changed over this last decade: how are the radial velocities extracted from the observed spectra and which additional measurement can be implemented? Which corrections should be applied to the extracted time-series data? How does stellar magnetic activity phenomena such as dark spots, bright faculae, or granulation cells, affect the observed spectra? What can we know about the stellar activity of the host star, how can we extract that information, and which tool can be best used to mitigate those effects?

The advances we have made over the years in those questions, and our participation and exclusive data access to surveys such as HADES and CARMENES requests a revisiting of this approach using tools such as Exostriker or Juliet in order to find new, yet undetected exoplanets.

Neutron stars are one of the most compact known objects in the universe. Their properties, such as masses, radii, magnetic fields or rotation, strongly depend on the dense phases of matter, from quarks to hadrons, in their interior. Thus, it is of high interest to comprehend the behavior of matter in the core of neutron stars so as to properly understand the different neutron star observables.

In this thesis we will study the impact of considering the transport coefficients (mainly the bulk viscosity) of the matter composing neutron stars, as these might affect the damping of the density oscillations that occur in the merger of neutron stars. The aim is to predict the damping of these oscillations after the merger of neutron stars, which will depend on the neutron stars’ composition. It is expected that this damping will be measured thanks to the gravitational wave detectors.

Compact stars, and more particularly neutron stars, are a unique laboratory for testing matter under extreme conditions. Over the past years a particular effort has been invested in studying different scenarios for the dense phases of matter in the core of neutron stars, from quarks to hadrons at high densities. The final aim is to understand neutron star observables, such as the mass, radius, magnetic fields or rotation, in terms of a plausible scenario for its interior.

For this purpose, theoretical approaches based on effective field theories for hadronic and quark matter have been developed in our group. The master thesis proposed aims at following the study of the interior of neutron stars by applying the previously developed theoretical frameworks to obtain the equation of state and transport properties of dense matter in the core of neutron stars. With these ingredients, we will be able to address the mass and radius of neutron stars as well as the dynamical properties of neutron stars, going from rotation to the effect of magnetic fields onto neutron stars.

Supermassive black holes of 109 solar masses are found at the center of most massive galaxies. These galaxies and their black holes are thought to grow synchronously at the same time. Under this scenario, the supermassive black holes would have evolved from seed black holes of smaller mass formed in the early Universe. Detecting such seeds when the Universe was very young is extremely challenging but with the advent of cutting-edge observations we can now reach distant galaxies hosting black holes that are actively accreting matter: active galactic nuclei (AGN). Recently, a few AGN have been observed in dwarf galaxies when the Universe was much younger than it is today, 6,000 million years after the Big Bang. The black holes powering this distant AGN are found to be more massive than expected from a synchronized growth with their host galaxies (i.e., they are over-massive black holes), a result that challenges models of black hole-galaxy co-evolution (Mezcua, Siudek et al. 2023; Mezcua et al. 2024).

This TFM aims at identifying AGN in dwarf distant galaxies and estimating their black hole mass. The results can confirm if distant AGN dwarf galaxies host over-massive black holes or whether instead, they host black holes of smaller mass. The latter could represent the relics of the early Universe seed black holes from which supermassive black holes form. This project will have important implications for our understanding of the seed black hole formation. A basic knowledge of python is advisable.

Galaxy mergers are known to play a key role in galaxy evolution. Mergers impact strongly both the galaxy morphology and kinematics, and can trigger/fuel the activity of the nuclear supermassive black hole (or active galactic nucleus, AGN). The detailed mechanisms driving this process are however not clearly understood. In the massive galaxy regime, galaxy mergers are found to be correlated with AGN activity. Although there is plenty of evidence that dwarf galaxies can also host AGN, it remains unclear how mergers affect AGN triggering in the dwarf galaxy regime, mainly due to a lack of dedicated studies.

The student will analyze Chandra X-ray observations of a sample of seven dwarf galaxy mergers that have AGN optical signatures. The presence of star formation and the low resolution of the optical data prevent from confirming the presence and location of the AGN. The aims are to detect AGN X-ray emission and to investigate the properties of AGN at different stages of dwarf galaxy mergers. So far no dual AGN have been confirmed in dwarf galaxy mergers, so the discovery of even one dual AGN would constitute a major breakthrough. The results can also provide valuable information for understanding the impact of mergers on AGN activity and black hole growth in the dwarf galaxy regime.

Consistent theory of the universe evolution should include all known epochs from inflation to dark energy.
This maybe done effectively only within some modified gravity like F(R) gravity or Gauss-Bonnet gravity,etc., for review see S.Nojiri, S.D. Odintsov and V.K.Oikonomou,"Modified Gravity Theories on a Nutshell: Inflation, Bounce and Late-time Evolution," Phys. Rept. \textbf{692} (2017), 1-104 doi:10.1016/j.physrep.2017.06.001
The topic of current thesis will be precise realisation of this picture via the reconstruction of scale factor. This will require some theoretical knowledge and computational methods.

Type II core-collapse supernovae (SNe) are the tremendous explosions that mark the end of the lives of massive stars that retained a hydrogen-rich envelope until their death. Although most massive stars are born in close binary systems, where they are expected to exchange mass or even merge with a companion during their evolution, it remains challenging to determine which Type II SN progenitors experienced binary interaction prior to collapse. SNe in the local Universe provide a unique opportunity to investigate their evolution, as they can be studied with independent observational constraints, including properties of their host galaxies and direct progenitor detections in pre-SN images. Apparent discrepancies between the inferred properties of the progenitor star from these different methods can point toward a binary-interaction history. In this Master’s project, the student will focus on Type II SNe with empirical constraints from multiple sources, combining host-galaxy information from observations in the PMAS/PPak Integral-field Supernova Hosts Compilation (PISCO) with literature detections of progenitors from the Hubble Space Telescope, with the goal of identifying possible clues of binary interactions.

Since 2018, the Zwicky Transient Facility (ZTF) has discovered hundreds of thousands of supernovae and other optical transients, providing an unprecedented database of light curves with high cadence and long temporal coverage. This rich dataset opens the door to systematic searches for subtle features in supernova light curves that may reveal clues about their progenitor systems. In particular, small-scale undulations or bumps have been suggested as possible signatures of binary interactions prior to explosion, such as mass transfer or envelope instabilities. In this Master’s project, the student will collect and analyze all available ZTF transient light curves, applying statistical and visualization techniques to identify and characterize such deviations from smooth evolution. The ultimate goal is to assess whether these light-curve modulations can be linked to binary progenitor scenarios, thereby improving our understanding of the final stages of massive star evolution.

Type Ia supernovae (SNe Ia) and tidal disruption events (TDEs) are both luminous transients that can show similar colors and light-curve evolution during their early phases, making them difficult to distinguish without prompt follow-up. Since SNe Ia are the main contaminants in TDE searches, especially during the rising phase and in the absence of very blue photometry, systematic comparisons between the two classes are essential. In this Master’s project, the student will make use of public ZTF data to measure and document the rise times and rising colors (e.g., g–r) of SNe Ia and TDEs. The goal is to identify statistically significant differences between the populations that can serve as early-time diagnostics for TDE classification. Such results will provide valuable input for automated querying scripts and machine-learning classifiers, paving the way for rapid spectroscopic identification of TDEs in the upcoming LSST era. 

PhotSat is a project to follow-up the photometry of bright stars from space. It will consist of two telescopes of 10 cm aperture integrated in a cubesat that will monitor the full sky for 2 years. It will provide the photometry of stars from the UV to the optical wavelengths in three different broad-bands. These time series will be used to characterize the variability of stars and it will also be possible to detect transiting systems, either binary systems composed by stellar objects and brown dwarfs, or planetary systems.

In this project we aim to analyse the characteristics of the multiple systems that could be detected with PhotSat, based on the cadence of the observations and the performance of the instrument. The student will be in charge of producing realistic simulations of light curves for a diversity of binary and planetary systems, and devising the strategies to detect the transit signals.

Eclipsing binary systems are excellent laboratories to understand the structure and evolution of stars. The analysis of the photometric mutual eclipses and the radial velocity curves provides information about the size and the mass of the components. Besides, the monitoring of the times of eclipse allows to study the internal distribution of mass of the stars and also to infer the presence of additional bodies orbiting around the system, potentially planets.

The goal of this project is to seize the opportunity that precise photometry obtained from space, such as that coming from TESS, offers to improve the analysis techniques and to study different eclipsing binary stars, either to determine the properties of their components or to study their eclipse time evolution.

The recent detections of gravitational waves (GW) from merging binary black holes (BBHs) have opened a new window on the Universe. Yet, a fundamental question remains largely unanswered: where do these systems form and merge? Are BBHs the end products of massive stars in star-forming galaxies, or do they preferentially arise in globular clusters, galactic nuclei, or even in more exotic environments such as primordial black hole populations?

In this project, we will explore how to use large-scale structure as a tracer of BBH environments. By cross-correlating the sky distribution of BBH mergers detected by LIGO-Virgo-KAGRA and future GW instruments with galaxy surveys (e.g., DESI, Euclid, LSST), we aim to statistically infer the host halo masses and large-scale environments of these events. The student will:

• Learn about gravitational-wave astrophysics, large-scale structure, and galaxy clustering.
• Develop and apply cross-correlation techniques to simulated mock galaxy catalogues and real data at PIC (CosmoHub).
• Constrain models for the astrophysical origin of BBHs and primordial or relic BBHs.
  Assess the impact of this population on galaxy formation and clustering, connecting to upcoming survey data from Euclid and ARRAKIHS.

This project is well suited for students interested in cosmology, gravitational waves, and data analysis. It will involve both theoretical modelling and hands-on statistical methods, providing training that is highly relevant for future careers in astronomy and cosmology.

Traditional black hole models describe a one-way journey toward a singularity — a region where the laws of physics break down. Recent theoretical developments, however, suggest a radical alternative: a collapsing region inside a black hole could undergo a bounce, avoiding the singularity and re-expanding into a new universe. This scenario, known as the Black Hole Universe (BHU) model, replaces the classical Big Bang singularity with a Big Bounce emerging from the collapse of a finite overdensity in a larger “parent” universe and could be use to model our universe.

In this project, the student will investigate one of the most intriguing predictions of the BHU model: the formation of relic black holes during the collapse phase. These objects could contribute to the dark matter we observe today, potentially influencing the formation and evolution of galaxies. The student will:

• Develop a mass function model for relic black holes generated during the collapse.
• Explore their cosmological implications, including their possible role as dark matter and the relation to observed binary black holes.
• Assess the impact of this population on galaxy formation and clustering, connecting to upcoming survey data from Euclid and ARRAKIHS.

This project will combine theoretical modeling, cosmological simulations, and statistical analysis, providing a deep introduction to modern problems in cosmology and gravitational physics. It is ideal for students fascinated by early-universe physics, black holes, and the nature of dark matter.

The concordance model in cosmology, called LCDM, has been accurately reproducing the main cosmological observations for many years. However, the nature of its two main components, dark matter and dark energy in the form of a cosmological constant, remains still unknown. Moreover, recent analyses, like the ones performed by the DESI Collaboration, show that dark energy could be evolving and deviate from a cosmological constant. It is therefore important to test models beyond LCDM against observations. When studying dark energy, one usually assumes dark matter as a perfect fluid with no pressure. In this project we aim at considering the latest cosmological observations to test not dark energy, but whether or not dark matter could have some pressure. In a second stage, we also aim at redoing the analysis done by the DESI Collaboration to test the evolution of dark energy while allowing for dark matter with some pressure at the same time. This will show us how much evolving dark energy depends on the nature of dark matter.

Massive stars are powerful cosmic engines, driving the cycle of matter in galaxies. Their strong winds and intense radiation carve vast bubbles into the surrounding gas, creating circumstellar nebulae that evolve with the star’s life. The size and shape of these bubbles depend both on the star’s winds and on the conditions of the environment around them. As the star changes, its bubble reflects those evolutionary stages, and, ultimately, sets the stage for the star’s explosive death as a supernova.

When the star finally explodes, the expanding remnant may contain a rapidly spinning neutron star, or pulsar. This pulsar can unleash a stream of high-energy particles, creating a pulsar wind nebula, like the famous Crab Nebula. To understand these spectacular nebulae, we must first understand the physical properties of the parent bubbles in which they are born.

The Multi-Messenger Astrophysics (MAP) group is at the forefront of exploring how the life and death of massive stars shape their surroundings. This project invites a Master’s student to join this effort, using advanced hydrodynamical simulations with the state-of-the-art PLUTO code to study the growth and evolution of stellar wind bubbles. In particular, we will together investigate how a star’s metallicity, its chemical make-up, affects the formation of these bubbles. This will, for the first time, provide a time-dependent view of how massive stars sculpt their environments in different galaxies, including the Milky Way, the Magellanic Clouds, and the extremely metal-poor Zwicky 18.

By studying these cosmic structures, you will help reveal how massive stars prepare the very cavities that will later host pulsars, unlocking new insights into the most powerful life cycles in the universe. 

Cosmological simulations are key ingredients to prepare and exploit the new generation of galaxy surveys. Typically, large supercomputing resources are required to develop high-fidelity simulations that can accurately mimic the large-scale galaxy distribution we observe. As the quality of the new observational data improves, the level of detail reproduced by numerical simulations already demands prohibitive computational resources.  Alternatively, clever ways of modeling the evolution of the large-scale structures in the universe have been proposed to achieve promising results at a much lower cost.  

We propose to explore new methods, based on Machine Learning techniques, to model both the large and small scales of the so-called “cosmic web”, allowing for a fast and efficient way to optimally exploit the new generation of cosmological surveys that promise to unveil the nature of dark-matter and dark-energy in the universe.

Gravitational Wave Astronomy began in 2015 with the first detection by the LIGO observatory of the gravitational wave emission produced by the collision of two black holes (Nobel Prize in Physics 2017). To be able to detect gravitational waves from binary systems of supermassive black holes (millions of times the mass of the Sun) we need to go to space. The European Space Agency is leading the LISA mission that will be launched in 2035 and has just entered the implementation phase. To be able to detect these systems, precise gravitational emission waveforms and efficient data processing algorithms are needed.

The aim of this project is to consider "simple" models of gravitational wave emission from binary systems of supermassive black holes when they are close to the collision phase and to design efficient algorithms for searching for these signals in synthetic data. In particular, these models must support the use of automatic differentiation for efficient calculation of gradients, so that they can be used with Bayesian inference methods that use these gradients, such as Hamiltonian Monte Carlo methods. In this sense, it is also important to incorporate the response of the LISA observatory in an efficient way.

The project will consist in the following tasks: (1) Optimization of some gravitational waveform generation models for binary black hole systems and verify that the application of automatic differentiation is efficient and gives reliable results. (2) Adapt the LISA response for these gravitational wave models in such a way that the use of automatic differentiation is possible. (3) Study the methods of sampling by Bayesian inference,  in particular the Hamiltonian Monte Carlo method. (4) Apply these methods, using open sources, and study the efficiency of models that allow the use of automatic differentiation in comparison with traditional models.

X-ray binaries (XRBs) are systems in which a compact object accretes material from a low-mass companion star. These binaries exhibit complex (and often dramatic) variability in their X-ray emission. Their behavior provides key insights into the physics of accretion, the launching of jets and outflows, and the interaction between compact objects and their environment under extreme physical conditions. 

With the recent launch of the Einstein Probe satellite, we are entering a new era in the study of XRBs. Thanks to its wide field of view and high sensitivity, Einstein Probe enables regular monitoring of both newly detected and well-known systems across the Galaxy, capturing their temporal evolution with unprecedented detail.

In this project, you will work with Einstein Probe data to investigate the variability properties of selected XRBs, characterizing their flux changes and interpreting the results within the broader framework of variability and accretion processes in XRBs.

In Astrophysics, the study and characterization of chemistry is commonly related to the search for (new) molecular species in astronomical environments and to the characterization of their properties (e.g., molecular abundance, temperature, spatial distribution, kinematics of the gas associated with them). Based on this, Astrochemistry has two major goals: (1) to use chemical species and their properties to derive key (physical) knowledge of the sources themselves (e.g., their mass, their temperature, their dynamic stage), and (2) to understand how chemistry evolves in time, increasing in complexity until eventually resulting in the formation of (pre-)biotic molecular compounds.

The goal of this project is to use state-of-the-art astronomical observations (with e.g., ALMA) to characterize and understand star-forming regions. For this, we use spectral line observations that allow us to determine the physical status of dense cores and clouds where the new generations of stars and planets form. Moreover, by studying the chemical composition of different objects we can search for evolution in the chemical content. Finally, observations of different objects throughout the Galaxy enables the search for chemical differences in the inner and outer regions of the Galaxy.

Most stars do not form in isolation, but in rich clusters containing hundreds of stars that are initially deeply embedded inside large molecular clouds. Therefore, understanding the formation of stars, and eventually planets, requires characterising the fragmentation process of the molecular cloud into dense cores out of which stars will eventually form. The number of newly-formed stars with a given mass, the so-called IMF (initial mass function), is a key parameter in the study of the formation and evolution of clusters that transcends all astrophysical fields. During the last decades, some theories have claimed that the IMF is related to the masses of the dense cores that fragment out of the molecular cloud, the so-called CMF (core mass function). The first observational results supported this possible connection, however biases and limitations on the spatial resolution and the accuracy of core mass determination may have affected the results obtained so far.

We seek to overcome these known observational biases and perform a detailed study of the CMF in four embedded clusters at different evolutionary stages, emerging from the same molecular cloud. For this, we have acquired ALMA band 6 (220 GHz) observations, reaching a spatial resolution of 200 au and a mass sensitivity of 0.1 Msun, which will allow us to resolve all the members in the cluster and probe the CMF from low to high core masses. The student will work with new high-quality ALMA data to produce images of the dust continuum emission of the dense cores in these four clusters. Following the generation of the astrophysical images, the student will use automatic procedures to identify and extract all the detected cores. This will allow us to determine properties such as their masses, sizes and location within the cluster. With the masses, the CMF of these clusters will be constructed and compared to the IMF.

Since the first detection of gravitational waves in 2015, gravitational wave astronomy has been transforming fundamental physics and astrophysics.

In order to decode the observed gravitational wave signals and identify their sources (e.g. to determine the nature of the compact objects as black holes or neutron stars, or measure their masses and other properties), one needs to compare theoretical models of the waveforms with the observational data using the methods of Bayesian statistical analysis.

The scientific insight that one can gain from the signals is thus not only limited by the  sensitivity of the detectors, but also the accuracy of our waveform models. Such models are synthesised from information obtained from  different perturbative approaches to solve the Einstein equations, and from large scale numerical simulations of compact binaries in general relativity.

The aim of the project will be to improve waveform models developed within the phenomenological waveforms approach, which has produced several families of computationally efficient codes that have been used by the LIGO-Virgo collaboration to analyse all gravitational wave events detected to date. Together with the student a suitable building block of the next generation of waveform models will be selected, which could focus on aspects such as high mass ratios, the effects of spin precession, or orbital eccentricity. From a technical point of view, the project could either focus on modelling simulation data, to carry out simulations, or on the development of efficient code implementations, e.g. using GPUs.

Stellar wind bubbles are the circumstellar nebulae forming around massive stars when their
supersonically-expanding feedback interacts with the interstellar medium of galaxies. Their formation
and evolution is mainly governed by the stellar wind properties and by the local conditions of the
ambient medium (Weaver et al. 1977, Wilkin et al. 1996). Throughout the star’s life, they reflect their
central star’s evolutionary phase and they shape the environment where high-mass stellar objects will
die by exploding as a supernova. This generates a supernova remnant, inside of which a strongly
rotating magnetised neutron star, or pulsar, might form and release its relativistic wind, giving birth to a
pulsar wind nebula. Hence, the understanding of pulsar wind nebulae requires first a comprehension of their parent environment (Meyer & Meliani).

Such environments can only be probed by numerical simulations. This Master’s project aims at
investigating the formation of circumstellar nebula around massive stars. It will, by means of 1D
hydrodynamical simulations, study the evolution of stellar wind bubbles of high-mass stars using the
in-house setups for the PLUTO code (Mignone et al. 2012). We will explore the effects of the
metallicity onto the formation and evolution on these stellar environments, obtaining thus a time-
dependent picture of how massive stars shape their surroundings, e.g. in the small and large Magellanic
clouds, and we will pronounce on the properties of the cavity that will later host young pulsars.

Stellar activity poses a major limitation to the extraction of planetary signals from radial velocities and transits. An evolving and rotating inhomogeneous star surface hampers the detection of small planets in temperate orbits and also atmospheric characterization of exoplanets using transit spectroscopy. Our ability to account for these effects is closely related to improving our understanding of stellar activity as a function of time and wavelength. This project will develop methodology to retrieve planetary signals from data affected by activity. One of the main tools will be the StarSim code, which is capable of accurately simulating stellar variability effects. Among other sources, proprietary data from the CARMENES radial velocity spectrometer will be analyzed.

The SPMN-CSIC network records about 20,000 meteors and fireballs every year, occurred over continental and insular Spain and neighboring countries. These luminous phenomena are produced by the arrival at hypervelocity of asteroidal and cometary rocks suffering ablation, and creating an ionized column called meteor. All these recordings are stored into the SPMN-CSIC meteor and fireball database that has been operational for 27 years. The goal of the proposed TFM is providing the candidate the basic skills to complete the astrometric measurements using a well-tested routine, and to learn the full reduction process of these data for obtaining valuable scientific publications. Then, using meteor recordings from several CCD and video stations all over Spain and Catalonia, we will be able to reconstruct the atmospheric trajectories, to obtain the radiant and incoming velocity, and finally to infer the heliocentric orbit in the Solar System of the meteoroids producing the recorded meteor events.

I envision a practical TFM, teaching the right methodology, defining several case studies and establishing a publishing goal to be achieved. All together to provide an appropriated student training, by gaining the required skills to continue a PhD thesis on this subject.

Requirements: A significant knowledge of English and Python are needed to be considered.

Pulsars are fast-rotating neutron stars with enormous magnetic fields usually observed by their radio emission, which reaches us in periodic pulses. A small fraction of the known population of pulsars have also been detected to emit high-energy emission, X- and gamma-rays, but the origin and some key properties of this radiation are still not well understood. In our group we face this question by developing effective radiative models that reproduce in a simple but realistic way the dynamics and emission of the charged particles responsible of this radiation, assuming synchro-curvature as the emission mechanism at work. We generate theoretical energy spectra and light curves and compare them with observational data, in order to constrain some parameters of the models and try to obtain information about the high-energy emission of pulsars.

During this thesis the student will 1) learn the theoretical basics of pulsar high-energy emission, 2) get to know the radiative models we develop and the numerical tools we use in our group and 3) apply them to some real cases, first obtaining some basic results and afterwards testing the impact of model parameters not deeply explored so far. In addition, the student will have the opportunity to develop strong computational skills by moving the actual structure of the numerical tools into a more modern one based in Python.

The Cosmology and Extragalactic Astronomy groups at ICE-IFAE-PIC have a long expertise on generating mock galaxy catalogs for several large extra-galactic surveys ongoing or being in which we actively collaborate such as PAU, DES or Euclid. In order to fully exploit and interpret the observed data from galaxy surveys it is essential to produce mock galaxy catalogues since they can help in a variety of ways. They are useful to design and calibrate galaxy surveys. They can help to study selection effects, to calibrate errors and explore systematic effects, to test new techniques to measure cosmological parameters or to calibrate cluster finders and photometric redshift estimators.

Accurately reproducing observed distributions in simulations is mandatory to achieve successful scientific analysis. In this Master thesis project we propose to apply in a novel way a method to estimate a continuous transformation that maps one N-dimensional probability density function distribution to another. This method will allow not only to reproduce the observed distributions but also to maintain the correlations between the observables. We will apply and validate this methodology using MICE and/or Euclid simulations.

Protostars are the progenitors of stars. They are star forming objects that intensively accrete mass from their immediate environment, more precisely from the circumstellar disk and the infalling envelope. Studying them thus allows us to understand how do stars acquire their fundamental properties of mass, angular momentum, and magnetic field. The protoplanetary disk (the future planet hosting disk) also forms during the protostellar phase, meaning that studying the evolutionary processes at play in these systems is crucial for our understanding or both star and planet formation.

The accretion of mass onto the central protostellar is accompanied by the launch of high velocity (~100-200 km/s) jets, carrying mass (~10^-7 to 10^-9 solar mass per year) and angular momentum form the central regions. The goal of this project is to study the jet launched by a prototypical young protostar located in the Serpens star forming cloud, with the synergy of deep ALMA high-angular resolution and recent JWST spectroscopic observations. Both ALMA and JWST data present very complementary emission spectral lines of the shocked structures propagating within the jet. ALMA data offers high spectral resolution lines that will characterize the dynamic of the jet (velocity, structures, evolution), and the JWST data spans several shock tracers (H2, FeII) and ionization tracers (NeII, ArII, SI) that will allow to study the excitation and chemical content of the gaz.

The student will start by reducing and imaging the ALMA and JWST spectroscopic observational data, then study the velocity/amplitude/structure of the emission lines seen in both datasets. Secondly, modeling of these emission lines will be performed to constraint the physical parameters (chemical content, velocity, irradiation, density, magnetic field) and dynamics (structures, evolution) of the shocks. This work will involve the use of pipeline reduction scripts (ALMA and JWST pipeline in python), of data analysis scripts (looking at spectral data cube in python), and of physico-chemical models (in batch and python).

The conjonction between these two cutting-edge datasets is unprecedented and will provide for the first time a compete view of the shock internal structures within a protostellar jet, which in turn will improve our understanding of the physical mechanisms launching these jets, and how do they impact the surrounding interstellar medium. 

Hydrogen-rich supernovae (SNe) showing long-lasting narrow emission lines in their spectra are known as SNe IIn. These objects arise from massive stars undergoing core collapse within a dense hydrogen-rich circumstellar medium (CSM). The source of the narrow emission lines is believed to be the result of interaction between the SN ejecta and this CSM, related to progenitor mass–loss episodes before the explosion. The physical mechanisms driving the mass loss are still not well understood. However, the environments where SNe explode can provide helpful information about these mechanisms and their progenitors. Therefore, this project aims to explore the environments of SNe IIn to constrain the progenitor properties of these interacting SNe and the role of metallicity in mass loss. The student will work with observations (data from the literature), astronomical tools, and Python codes.

Hydrogen-rich supernovae (SNe II) are produced by the final explosion of massive stars (>8 Msun). They retain a significant fraction of hydrogen at the moment of the explosion, and hence their spectra show prominent Balmer lines. Recently, SNe II have been proposed as metallicity indicators, making them relevant in the cosmic context. More precisely, theoretical models predict that the strength of metal lines around 50 days post-explosion is related to the metallicity of the SN progenitor [1]. Thus, SN II metal-line pseudo-equivalent-widths (pEWs) generally become stronger when metallicity increases. This project aims to explore this correlation and test the parameter space obtained from the models. The student will work with observations and models, astronomical tools and python codes. The sample used for the project comprises spectra from the literature and new observations, plus theoretical models developed by Dr Luc Dessart.