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Supervisor(s): Boris Deshev

This doctoral project focuses on the development and scientific application of LEIDMA, a machine-learning framework designed to detect extremely weak signals in radio astronomical observations. The work combines methodological innovation with astrophysical research, addressing both the technical challenges of analysing modern large datasets and a fundamental open question in galaxy evolution: how galaxies acquire gas from their surroundings.

The first part of the PhD will concentrate on designing, training, and optimising neural-network models capable of identifying faint spectral signatures that remain undetected with conventional analysis techniques. Using curated training datasets and archival observations, the student will develop robust and reproducible workflows for automated signal extraction, contributing to the preparation of analysis tools required for next-generation facilities such as the Square Kilometre Array.

The second part of the thesis will apply these methods to deep radio surveys, including archival Arecibo observations and complementary HI datasets, to investigate the presence and properties of diffuse neutral hydrogen in the circumgalactic and intergalactic medium. Through statistical signal detection and stacking analyses, the project aims to place new constraints on gas accretion processes across different galactic environments.

By combining advanced data science with observational astrophysics within an international collaboration, the project will produce both a publicly available analysis tool and new scientific results, while providing comprehensive interdisciplinary training for the doctoral candidate.

Supervisor(s): Mait Lang, PhD

The study uses Sentinel-2 MSI, Landsat-8/9 OLI data, and the airborne laser scanning (ALS) database provided by the Estonian Land board. To prepare, the study analyses also last 40 years records of landscape fires to collect information of fire behaviour. Time series of multispectral satellite images and ALS data are used to construct input maps for landscape fire behaviour models (starting with https://cwfis.cfs.nrcan.gc.ca/background/summary/fbp) and the models will be tested on the observation data from known vents. Finally, scenarios are simulated for test sites and geographic locations in Estonia where high forest fire risks occur. The results will establish basis for the integration of remote sensing data analysis into strategic, tactical and operational planning.

Supervisor(s): Mihkel Pajusalu

The goal of this PhD project is to research, benchmark and compare various machine learning algorithms and their deployment strategies, focusing on FPGA (Field Programmable Gate Array) SoCs (System on Chips) as computing platforms and considering both ground and space segments. An important aspect of this will be remote reconfigurability and the mission control operations needed to enable it.

The main deployment target in this project will be the FPGA SoC based Command Module payload built by the Estonian Student Satellite Foundation, planned to be launched on the ESTCube-3 CubeSat (development began in 2025, launch planned for 2028). As the payload will be built to be reconfigurable for multiple experiments in different data processing domains, it will provide an excellent opportunity to conduct this project and to test the developed framework on various use cases.

In addition, it is intended for this research to result in a new framework for testing, benchmarking and deploying machine learning algorithms independent of the target hardware and utilizing different sensors and instruments depending on the use case. The resulting product will speed up the development of machine learning algorithms for satellite autonomy applications and simplify the mission operations.

Supervisor(s): Heleri Ramler, Eike W Günther

This PhD project investigates the connection between Galactic evolution and the formation and occurrence of exoplanets around early-type stars. Different regions of the Milky Way have experienced distinct star-formation histories, chemical enrichment, and dynamical evolution, which are expected to influence the conditions under which stars and planetary systems form.

The project focuses on early-type A stars, which remain underrepresented in exoplanet studies compared to Sun-like stars. Due to their higher masses, different internal structures, and stronger radiation environments, early-type stars provide a unique opportunity to explore planet formation and evolution under physical conditions that differ from those of late-type stars.

By combining precise Gaia astrometry and kinematics with high-resolution stellar spectroscopy, the project will compare early-type stars with and without detected planets across different Galactic environments. The analysis will examine how stellar kinematics, chemical composition, and Galactic context relate to the presence and properties of planetary systems.

The results of this work will extend planet–star–Galaxy studies into a previously unexplored regime, providing new constraints on how Galactic-scale processes shape planet formation around intermediate-mass stars and informing future exoplanet surveys and space missions.

Supervisor(s): Antti Tamm, Rien van de Weygaert

In recent years, cosmology has achieved high precision in determining the general properties of the Universe, yet we remain far from a complete understanding of galaxy formation and evolution. The unexpectedly massive galaxies recently discovered in the early Universe with the James Webb Space Telescope demonstrate the gaps in our knowledge regarding matter assembly and star formation processes. Cosmic voids provide a unique opportunity to study galaxy formation and evolution in an environment where the large-scale matter density is very low, galaxy formation proceeds with significant delay (thus occurring closer to us and being more easily observable), and external influences are weaker.

The doctoral project investigates the role played in the evolution of galaxies located in voids by their immediate surroundings — the galaxy group — and the role of the general largescale environment — the cosmological void. The results will contribute to a better understanding of the physical processes shaping galaxy formation and evolution, including the interplay between dark matter and dark energy in different environments and at different stages of cosmic evolution.

Central to the research is the unique dataset provided by the ongoing J-PAS survey, containing information on the properties and spatial distribution of millions of galaxies. The results will be compared with cosmological simulations in order to verify the correctness of the methods and to test the validity of cosmological models.

Supervisor(s): Krista Alikas, Riho Vendt, Viktor Vabson

Water leaving radiance is a key parameter for ocean colour (OC) satellite radiometry. It is the basis for higher order products (e.g. chlorophyll a) and subsequent spatiotemporal analyses. Measurement schemes for in situ above-water radiometry are already well addressed, but in-water measurements, despite considered more accurate, still need attention. Optical laboratory facilities at Tartu observatory will be advanced to allow the characterization and calibration of in-water radiometers, assuring the traceability of a measurement and uncertainty budget derivation when moving from controlled laboratory to variable outdoor conditions. Outdoor comparisons of common radiometers in various deployment strategies, together with the development of new sensor prototype will help the community to optimize the in-water measurements. This allows producing traceable in situ measurements required for every OC satellite mission for validation, vicarious calibration and algorithm development.