master thesis uit

LaTeX document class for writing theses

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Manual: thesis front page

What is it.

The UiT thesis LaTeX template has built-in support for typesetting the standardized front pages for master theses and PhD dissertations as defined by UiT’s current graphical profile. The UiT logos and the default graphical patterns are included as vector graphics (unlike the Microsoft Word templates that are available from UiT’s web pages) to ensure the highest quality result both in print and when published electronically as PDF.

In addition, the UiT thesis LaTeX template allows you to customize the front page by replacing either of the graphical patterns with your own image(s).

How to use it?

The uit-thesis template uses the familiar \maketitle command (from the standard LaTeX book class) to typeset the front page.

The following macros are used to specify the front page text:

Note that the macros above must be specified before \maketitle . Moreover, they should be specified right after \begin{document} , and before \frontmatter and \mainmatter .

which results in the following:

Custom images

The standard graphical pattern on the front page is divided by a diagonal white line into two parts: a triangle (left part) and a trapezoid (right part). The uit-thesis template has support for replacing either both parts by a single image, or replacing either of the parts by a separate image.

The following image customization commands are available:

• Geosciences - master

Duration : 2 years

Duration: 2 År

• Study catalogue

As a master student in Geosciences at UiT The Arctic University of Norway you can choose among a large variety of topics that are relevant for the future of the society. You can choose topics related to past and present climate change, environmental geology, terrestrial and marine geohazards, the formation of mountain chains, as well as the formation and distribution of natural resources. As part of your thesis you can perform practical fieldwork on land or at sea, and/or carry out laboratory work using a large variety of methods and instruments, and/or focus on data analyses using a computer. The majority of topics offered for M.Sc. theses focuses on studies of areas in northern Norway, the Barents Sea, Svalbard and East Greenland, but also elsewhere in the world. After finalizing your M.Sc. thesis, you can apply for jobs in private companies, public authorities and research institutions all over the world or you can even start your own business.

Kai Mortensen

Rådgiver

• Telephone: +4777644428
• Campus: Tromsø

Margrethe Lindquist

Førstekonsulent

• Telephone: +4777625153

Information about semester start

Bachelor's degree in Geology (180 ECTS) or equivalent qualification, with a major in geology or related subject area of minimum 80 ECTS.

Applicants must have a minimum grade average comparable to a Norwegian “C” (2.5) in the ECTS scale.

Nordic applicants: 4007

EU / EEA / Switzerland applicants: 7104

Non-EU/EAA applicants: 2026

The study consists of a Master's Thesis (60 ECTS) as well as mandatory and elective courses (60 ECTS). The number of elective courses varies in regards to topic(s) of study. You can either follow courses at The University Centre in Svalbard , elsewhere in Norway or abroad.

The Master`s program in Geosciences is aimed at providing students with a solid in-depth education covering, e.g., marine geosciences, structural geology, petrology, mineralogy, ore geology, sedimentology, quaternary geosciences, petroleum geosciences and geohazards

The research undertaken in geology at UiT Arctic University of Norway focuses mainly on fieldwork in Northern Norway, in climate and marine geology with research cruises in the polar areas. We have unique geology right on our doorstep, which offers a special understanding of the processes that continue to shape the earth. Close cooperation with e.g. industry, the public sector and other research institutions ensures that you as a M. Sc. student gain experience in fields relevant for the job market.

In addition to a solid competence in a specialized field of geology, students will have acquired the following after completing the Master of Science in Geosciences:

The candidate has

• advanced knowledge within general geosciences and specialized insight in one or more of the following fields; marine geosciences, structural geology, petrology, mineralogy, ore geology, sedimentology, Quaternary geosciences, petroleum geosciences, and geohazards.

• thorough knowledge of scientific theory and geoscientific methods within the selected topic(s). This includes understanding geoscience based on scientific observations from the field and the laboratory.

• the ability to apply geoscientific knowledge in new technological or scientific areas within the selected topic(s).

• the ability to solve geoscientific problems on the basis of the scientific history, tradition, and distinctive character of geosciences. This includes deep understanding of geoscientific data from the field and/or the laboratory as well as understanding of geological time, processes, and products.

The candidate can

• critically analyze geoscientific problems using data, methods, and results from peer-reviewed, international geoscientific research literature. The candidate can also structure and formulate scholarly arguments building on such information.

• analyze existing geoscientific theories, use methods and interpretations, and work independently with applied and theoretical solving of geoscientific problems. This regards both the solution of academic research questions and applied aspects of the selected topic(s).

• use relevant geoscientific methods for research and professional development within the selected topic(s) in an independent manner. This can include field observations, data collection, laboratory analyses, and literature review.

• carry out an independent, well-defined research project under academic supervision. The project is based on geoscientific field observations, laboratory data, literature or a combination thereof.

• conduct his/her work in accordance with current ethical standards within the selected geoscientific topic(s).

General competence

• analyze and carry out the most important elements of academic and professional geoscientific research projects: penetrate literature, carry out field and laboratory research, analyze geoscientific data and communicate results to fellow students and scientists.

• apply his/her knowledge and skills in new areas for completing advanced tasks and projects within the selected geoscientific topic(s).

• communicate comprehensive independent work and master the geoscientific terminology as well as the use of key literature and academic referencing. This includes communication in the form of written academic theses, research reports, and oral presentations to peer students and geoscientists.

• discuss, argue, and critically assess geoscientific problems, analyses and conclusions, both with specialists and with the general public.

• contribute to academic, technological and industrial innovation within the geosciences.

Writing your M.Sc. thesis in geosciences at UiT qualifies you for a large variety of jobs in industry, public authorities and research institutions, both nationally and internationally. Or maybe you even want to start your own business? Our former students work, e.g. with

• Investigations of unstable rock slopes, rock-slope failures and flooding.
• Environmental investigations.
• Carbon capture and storage.
• Exploration and exploitation of natural resources relevant for the Green Shift.
• Searching for oil and gas.
• Own consultancy business.
• Gehazards at The Norwegian Water Resources and Energy Directorate (NVE).
• Geological mapping onshore and offshore at the Geological Survey of Norway (NGU).
• Onshore and offshore investigations in the Arctic and Antarctica at the Norwegian Polar Institute.
• Investigation and management of natural resources at the Norwegian Petroleum Directorate, as well as at the Directorate of Mining with the Commissioner of Mines at Svalbard.
• Planning of new roads and safe infrastructure at the Norwegian Public Roads Administration.
• Research and education at universities and research institutions.

To see the courses taught in the program, see “Study plan” for a full view of the course

Admission to the Master's programme in Geosciences requires a Bachelor's degree or equivalent qualification (180 ECTS) in Geology. Applicants must have a minimum grade average comparable to a Norwegian “C” (2.5) in the ECTS scale, see the UiT webpage for international admissions for more information on how the point average is calculated.

The Master`s programme in Geosciences has restricted admission and for the admissions autumn 2024 there are;

25 available spots for applicants from the Nordic countries

10 available spots for applicants from outside the Nordic countries

Applicants from Norway or Nordic countries:

• The application deadline for Norwegian and other Nordic applicants is 15th of April for admission to the autumn semester
• Online application, study code 4007
• Her finner du all informasjon knyttet til søking og opptak

Applicants within EU/EEA/Switzerland (except the Nordic countries):

• The application deadline for applicants within EU / EEA / Switzerland is March 1st for admission to the autumn semester.
• Online application, study code 7104.
• You will find more information about admission for EU/EAA applicants here .

Applicants outside EU/EEA/Switzerland and the Nordic countries:

• The application deadline for applicants outside the EU / EEA / Switzerland and Nordic countries is 15th of November for admission to the autumn semester.
• Online application, study code 2026.
• Non-EU students must be prepared to pay tuition fees, more information here
• You will find more information about admission for Non-EU/EEA applicants here

English language proficiency: Applicants with education from non-Nordic countries must document English language proficiency. You will find more information on English language requirements on the UiT webpage for international admissions.

Teaching methods consist of lectures in classrooms, laboratory work in modern lab facilities, fieldwork and cruises to interesting areas, mainly in Northern Norway.

Examinations will be given as written exams, oral exams and reports, depending on the individual course description.

The language of instruction is English and all syllabus material is in English. Examination questions will be given in English, but may be answered in either English or a Scandinavian language.

The Master's Thesis can be written in either English or a Scandinavian language.

Successful completion of a Master's degree in Geosciences qualifies a candidate for admission to PhD studies, depending on satisfactory marks in the Master's and Bachelor's degree. PhD studies in geology are offered at UiT Arctic University of Norway.

The Department of Geosicences has a close cooperation with The University Centre in Svalbard. (UNIS), which offers a broad range of courses in Arctic geology, geophysics and technology. For other exchange possibilities abroad, see the university's pages for student exchange .

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• Administrasjon/UB
•   Hjem
• Fakultet for biovitenskap, fiskeri og økonomi
• Norges fiskerihøgskole

Master's theses in international fisheries management

Vis alle innførsler i samlingen sortert på.

Forfatterliste Tittel Dato

Søk i denne samlingen:

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Aquaculture market, diversity, and economic development of Bangladesh for the millennium 1995-2019 

Identifying the Genetic Population Structure Knowledge Gaps Hindering an Improved Management of the Spurdog (Squalus acanthias) stock in the Northeast Atlantic: A Systematic Review 

[EFFECT OF CROWDING ON BEHAVIOUR OF ATLANTIC SALMON (Salmo salar) IN AQUACULTURE] [Using behaviour as a tool to assess welfare in Atlantic salmon aquaculture production] 

Assessing the conditions for compliance in the squid light fisheries in Myeik, Myanmar 

Effect of omega 3 fatty acid on growth in Atlantic salmon (Salmo salar) aquaculture 

Financial feasibility of seaweed farming in Norway 

Trends in the Sri Lankan Longline Tuna Fishing Effort (2015-2019): A GIS-Based Spatial-Temporal Analysis 

An analysis of the “on/off” Barents Sea capelin fishery: Implications for sustainable management of a fluctuating fish stock 

The bigger they are, the faster they fall, implications of size selective fishing on the spawning stock of Pandalus borealis in the Svartnes basin, (Balsfjord, northern Norway) as relates to size, age, growth, and abundance 

Nepalese Women Participation in Small Scale Aquaculture and Sustainability in Nepal: A Systematic Literature Review 

Cage fish farming for the livelihood improvement of the local people in Kulekhani, Nepal 

Asking for advice: A Review of the Special Request for ICES Advice of the years 2010 to 2020 

Cultivation of Saccharina latissima (Sugar kelp) using sea urchins crush as a nutrient source 

Seabed minerals in Norway. An analysis of conflicts and sustainability issues. 

“Public perceptions of pink salmon in Norway and willingness to pay for the current eradication management program” 

Effects of the Covid-19 pandemic on Spanish Cod market 

A GIS-based modelling approach to identify natural drivers of coral reef abundance in the Northern Myeik Archipelago, Myanmar 

From intention to consumption - A quantitative study on how Modular Food-Related Lifestyle interfere with Theory of Planned Behaviour when young Norwegian adults intend to consume seafood 

Capture based aquaculture and sustainability 

Challenges for implementation of the FAO’s Voluntary Guidelines for Securing Small-scale Fisheries. A case study of Small-Scale fisheries governance in India 

Master's thesis projects

If you are enrolled in a master's program and interested in writing your thesis in collaboration with the UiT Machine Learning Group, we propose some interesting projects you can get involved with. You may also contact directly one of the group members if you wish to work on a topic that is not list ed here.

Deep learning aided quantification in PET imaging

Positron emission tomography (PET) is a medical imaging technique that visualises the distribution of an injected radioactive tracer in living subjects. Medical imaging with PET plays an important role in the detection, staging, and treatment response assessment of many diseases, including cancer, neurological and cardiovascular conditions, as well as inflammation and infection.

PET is quantitative, in the sense that it allows not only visualisation but also non-invasive quantification of regional tracer uptake. Specifically, with dynamic PET imaging, it is possible to fully assess the time-dependent tracer distribution in the body. This allows quantification of biological processes, such as glucose metabolism or blood flow, by using tracer kinetic modelling. Tracer kinetic modelling, however, requires accurate determination of an arterial input-function (AIF), i.e. the tracer time-activity curve in blood.

The gold-standard AIF is obtained by measuring the time-dependent FDG radioactivity concentration in arterial blood through invasive blood sampling. This procedure is complex, time-consuming, and potentially painful with risk for complications.

The aim of this Master´s project is to develop novel methodology for arterial input function prediction, building upon state-of-the-art deep learning methods. In addition, the project will focus on developing interpretability and uncertainty methods to explain outcomes and the variability in the predictions. Both preclinical (mice) and clinical (human) PET data will be explored in the project.

Prerequisites:

Relevant machine learning courses, for instance FYS-2021 and FYS-3033

Programming skills in Python, preferably using PyTorch and/or TensorFlow

Experience with medical image processing is an advantage

Contact persons

Luigi Luppino ( [email protected] )

Samuel Kuttner ( [email protected] )

Graph neural networks

Graph neural networks are very powerful tools but have some limitations, like over-smoothing and over-squashing ( https://towardsdatascience.com/over-smoothing-issue-in-graph-neural-network-bddc8fbc2472 , https://towardsdatascience.com/over-squashing-bottlenecks-and-graph-ricci-curvature-c238b7169e16 ). The project will explore the causes of these limitations and try to propose solutions. 

Recommended prerequisites : Knowledge of machine learning and signal/image processing. Good programming skills (Python).

Contact person : Benjamin Ricaud ( [email protected] )

Hidden Markov Model Time Series Segmentation

Hidden Markov model remains the most used model for time series segmentation. Its main advantage, discrete hidden states and observations is also its main limitation. Of course, there have been several works extending to model to more versatile distributions. Our objective will be to train a recurrent network to segment time series by enforcing sparsity of the latent representation. All that in an unsupervised manner of course. We will investigate what does the model "naturally" extract and eventually how to guide it. Possible applications include medical data, electrical usage, NLP, and many others.

Recommended prerequisites : FYS- 3012 , FYS-30 33

Contact person : Ahcene Boubekki ([email protected])

Population counting using Drone Images for Marine Surveys

Marine surveys require use of valuable resources (expert's time and boats). UiT in collaboration with Norwegian Polar Institute and University of Southern Denmark is working towards developing a solution for performing population counting based on images captured from flying a drone. The initial plan is to develop a supervised learning based methodology for detecting the number of porpoises in an image. Later on, the plan is to further develop the framework to accommodate for other similar mammal species (with fewer training samples).

Prerequisites : FYS-2021, FYS-3033

Contact person : Puneet Sharma ([email protected])

Safe AI using Bayesian Deep Learning

Current decision support tools are usually designed by using expert knowledge or data driven techniques. However, these methods are mostly dependent on the high level of understanding of the subject or a dataset with unrealistic high quality to achieve optimal or desired performances. Many real-world problems are highly complex, which require new techniques that can model uncertainties and making decisions based on the availability and quality of data. Approaches toward building a personalized decision support tools include developing a prediction model of the risk and outcome, or deriving safe and effective data driven decision algorithms. With the development of artificial intelligence, deep learning has been used extensively in modelling and prediction. The combination of deep learning with Bayesian inferencing allows information and uncertainties to be accurately estimated from the training data. The AI agent needs to be designed carefully such that it can safely explore the environment and propose actions that are both risk-averse and robust. Integrating deep learning, Bayesian inferencing with reinforcement learning framework will bring great  opportunities to solve the problem and contribute toward a safe AI.

Background : A background in Bayesian inference, deep learning and reinforcement learning would be ideal, but a general background in machine learning and statistical methodology will be sufficient. Good programming skills are required.

Contact persons : Fred Godtliebsen , UiT Machine Learning Group and Phuong Ngo, Norwegian Centre for E-health Research

Reference : 

[1] Ngo, P. and Godtliebsen, F., “Data-Driven Robust Control Using Reinforcement Learning,” 2020. [Online]. Available: https://arxiv.org/pdf/2004.07690.pdf .

Precise Uncertainty Quantification in Dynamic Line Rating Forecasts

Description

In this master's thesis, the student will focus on developing a model to forecast line temperature, a crucial factor in Dynamic Line Rating (DLR) for energy grids. The model will utilize sensor data (specifically line current) and weather data, including both historical and forecasted information sourced from YR APIs. The data will be pre-processed and provided to the student. The key challenge is to not only accurately forecast line temperatures but also to precisely quantify the associated uncertainties.

The main objective is to create a model that accurately forecasts line temperature by using sensor and weather data. Emphasis will be placed on quantifying and controlling the forecast's uncertainty.

Model Development: Apply machine learning techniques with a particular focus in uncertainty quantification.

Uncertainty Analysis: Develop methods to rigorously quantify the uncertainty bounds in line temperature forecasts and their subsequent influence on DLR predictions.

Potential Impact

This project will significantly enhance DLR in energy grid management by providing more accurate and reliable line temperature forecasts. Such advancements will facilitate better decision-making, improving the efficiency and dependability of power distribution networks.

Prerequisites

A solid understanding of machine learning, deep learning, and statistical analysis. Proficiency in programming, ideally in Python. Background knowledge in energy systems or meteorological data analysis is an advantage but not essential.

Contact Person

Puneet Sharma ([email protected])

Industry partner: Avju Solutions, Jonas Mørch-Lampe ([email protected])

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The code produced for my master thesis at UiT, during fall 2019.

MadsAdrian/mastersthesis

Folders and files.

Repository files navigation

Uit-masterthesis.

The code produced for my master thesis at UiT, during fall 2019. Developed in collaboration with Luigi Luppino ( https://github.com/llu025 )

• Python 99.8%
• Dockerfile 0.2%

Template for a Masters or Doctoral Thesis

This LaTeX template is used by many universities as the basis for thesis and dissertation submissions, and is a great way to get started if you haven't been provided with a specific version from your department.

This version of the template is provided by Vel at LaTeXTemplates.com , and is already loaded in Overleaf so you can start writing immediately.

Checkout this short video to see how to easily create and edit new chapters as your thesis develops.

Please read the unofficial quick guide to the template; it contains some tips and suggestions on how to modify certain things.

(Updated 27/08/17)

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School of Urban Studies and Planning

Urban Studies Master Theses and Dissertations Defended in AY 2023-2024

by Toulan School June 17, 2024 Share

PhD Dissertation Defenses:

Maryam Amiri "Embodied Urban Political Ecology of Oil: Social Reproduction in Oil Geographies, Case Study: Ahwaz, Khuzestan, Iran" Chair: Dr. Megan Horst

Kyu Ri Kim "The Central Role of Safety Perceptions in Connecting Crash Risk Factors and Walking Behavior Chair: Dr. Jennifer Dill

Minji Cho "Three essays on communicative planning from the perspective of East Asians" Chair: Dr. Matthew Gebhardt

Huijun Tan "The Effects of Physical Accessibility and Subjective Accessibility on Grocery Shopping Behaviors in Oakland, California" Chair: Dr. Lisa Bates

Amanda Hudson "Participatory Democracy: The Potential for Civic Transformation, Understanding Participants’ Learning in the Milwaukie Citizens Jury" Chair: Dr. Dawn Richardson

MUS Thesis Defense:

Heather Bartlett "E Hui me ke Kaiāulu [To Connect with the Community] A Case Study on Implementation-Focused Community Planning Practices in Hawai‘i County" Chair: Dr. Lisa Bates

Bongani Ikaneng "An Analysis of Citizen Participation in Spatial Plan Preparation, Case Study Of Gaborone City, Botswana" Chair: Dr. Matthew Gebhardt

Nickolas Hash "The Foodpunk City: Advancing Portland’s Food Security Through a Shared Ideology of Adaptation & Resistance" Chair: Dr. Megan Horst

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