Tartu Observatory has launched a doctoral studies programme in Space Research and Technology.
The main application period will be from 1 to 15 May 2022. Admission is open for the following research projects (scroll down for abstracts):
- The response of lakes to changing climate: a combination of satellite and in situ data (supervisors Kersti Kangro ja Krista Alikas)
- Using dynamical friction to investigate the properties of dark matter (supervisors Rain Kipper, Elmo Tempel)
- Cosmology with J-PAS superclusters (supervisors Antti Tamm, Lauri Juhan Liivamägi)
- Comparison of traditional algorithms with neural network algorithms for use on spacecraft (supervisors Mihkel Pajusalu, Rene Laufer)
- Electric sail demonstration mission in solar wind (supervisors Andris Slavinskis, Pekka Janhunen)
In the Faculty of Science and Technology, all candidates must submit a motivation letter and a CV in the Dreamapply together with the application. Candidates will be assessed on the basis of a motivation letter and an entrance interview.
The maximum points for motivation letter and interview are 100, each giving up to 50 points. Candidates, whos doctoral thesis project was evaluated with at least 70 procent from the maximum (35 points), are invited to an interview.
Doctoral students admitted to these projects will have employment contracts with the junior researcher. The expected workload is 1.0, the expected work period is four years. The final workload will be set during the negotiations. Study module has been reduced to 30 EAP, the rest 210 EAP will be filled with doctoral students individual research plan.
Read more about status and funding of doctoral students.
Read more about language recuirements.
The response of lakes to changing climate: a combination of satellite and in situ data
Jupervisors: Kersti Kangro and Krista Alikas
Phytoplankton is the basis of the food chain and important component of food-web and carbon cycle in all water bodies, being the first component in the lake to react to the changing environmental conditions. In the boreal region the changes in phytoplankton community are predicted due to more frequently appearing ice-less winters and higher summer temperatures favouring cyanobacterial growth. This means more potentially toxic cyanobacterial blooms, which are affecting the lake functioning and public usage. Remote sensing gives a possibility to get an overview about bloom parameters for the entire lake, multiple lakes simultaneously and with higher frequency than national monitoring possibilities offer. EU Copernicus programme satellites Sentinel 3/OLCI and Sentinel 2/MSI allow to monitor Chl a changes in boreal lakes, with an inclusion of Lake CCI database changes in temperature in various lakes can be studied. This doctoral thesis focuses to phytoplankton community changes detected from in situ and satellite data, using hyperspectral data and microscopically determined phytoplankton community composition to: -Study cyanobacterial bloom parameters from Sentinel 3/OLCI and Envisat/MERIS in shallow eutrophic lake Peipsi -estimate changes in phytoplankton community in larger boreal lakes -detect community changes from hyperspectral parameters gathered from various lakes seasonally during multiple years, based on Estonian and Swedish data.
Using dynamical friction to investigate the properties of dark matter
Supervisors: Rain Kipper, Elmo Tempel
One of the main problems of astrophysics is the existence of dark matter, but not knowing what exactly it is. For solving it we need to study the dark matter as diversely as possible. A way with high potential, but not well explored is dynamical friction, which potentially allows to study the behaviour of dark matter on small scales. During this PhD project we study what conditions and where the effects of dynamical friction come forth best, select best candidates to study and study them from observations, and test what is the (relative) contribution from dynamical friction caused by baryonic matter and dark matter.
Cosmology with J-PAS superclusters
Supervisors: Antti Tamm, Lauri Juhan Liivamägi
Last decades have seen a huge progress in our understanding of the universe, largely thanks to the rapid development of observational facilities. According to the general undertanding we are living in a Big Bang universe, 69% of which constsits of dark energy, 26% of dark matter and a mere 5% of the normal baryonic matter, known to us. Unfortunately, numerous experiments and observational projects have not revealed, what dark matter is made of and what is the nature of dark energy. We have to admit that our theories of fundamental particles and gravity are either incorrect of incomplete. One key to solving this puzzle may lie in studying the largest structures in the universe, galaxy superclusters. In this project, data from the novel J-PAS cosmological survery will be used to map galaxy superclusters in the nearby and distant universe. The unique methodology applied in J-PAS grant an unprecedentedly deep and extensive astronomical dataset, with which we hope to clarify the role of large density pertubations in the formation and evolution of structures and check whether the current cosmological models support the existence and evolution of the largest supercluster complexes.
Comparison of traditional algorithms with neural network algorithms for use on spacecraft
Supervisors: Mihkel Pajusalu, Rene Laufer
The goal of this doctoral thesis project is to investigate the selection of machine learning algorithms for space application, comparison of them with more traditional algorithms and investigation on how the specifics of space applications affect this. In addition, we will investigate the effect of the space environment on the use of such algorithms and how the choice of the algorithm can affect the architecture of the whole system. The main goal of this is improvement of space mission scientific output in limited bandwidth scenarios and overall reliability of such systems. The study will be conducted while developing the OPIC (Optical Periscopic Imager for Comets, launch planned in 2029, delivery to the prime contractor in 2025) instrument onboard the ESA Comet Interceptor mission as a real application scenario. For this, it has to be evaluated which kinds of algorithms and especially machine learning approaches would be applicable for this mission. The goal is to maximise the scientific return through the limited resources available. This will require the study of various tools, but also the development of own deployment tools to cover as many options as possible and to be able to evaluate all import design options. Ultimately, the chosen design will be tested in the OPIC scenario and potentially in applications in other fields, such as Lunar rover development, will also be explored.
Electric sail demonstration mission in solar wind
Supervisors: Andris Slavinskis, Pekka Janhunen
Slavinskis and Janhunen have cooperated in electric sail demonstration since the ESTCube-1 project in 2011. They have been publishing ESTCube-1 mission design and results, ESTCube-2 mission design, FORESAIL-1 design, Aalto-1 design and results, and the MAT concept and design together. With the first two ESTCubes, the goal has been to demonstrate the E-sail in low Earth orbit (LEO) where it can also be used as a plasma brake for deorbiting. With ESTCube-3 and this project, we will close the gap between the LEO demonstration results and the future applications of the E-sail, such as MAT. This project will design the ESTCube-3 mission to demonstrate the electric sail in its authentic environment, the solar wind. Palos will improve and publish E-sail mission design tools and propose a secondary mission objective to flyby a near Earth asteroid.
erko.jakobson [ät] ut.ee