Master Programme in Applied Computational Science, Physical Geography
Lund University
Startdatum:
Slutdatum:
Studietakt: 100 %
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Kod: NATBE
<p>If you want to combine physical geography and advanced computations in your work, you may be interested in this programme. This Master’s programme gives you knowledge in how to use numerical simulations and machine learning to make forecasts and analyses, or to replace experiments. You can learn how to generate and store large amounts of data and use data science to search for patterns, connections, and trends in issues related to physical geography.</p><p>Advanced computations are increasingly important in research and business. On this Master’s programme you will learn how to study complex processes within the natural sciences, and how computational science can contribute to knowledge evolution in society. Some examples of angles to focus on could be environmental and climate change, global environmental issues and global cycles. In addition to theory for computational science, there will be an emphasis on obtaining knowledge about the practical tools that are used by professionals in the field, including training your skills in programming.</p> <p>The programme will prepare you for a professional career in academia as well as in business and industry. The Master’s programme gives you specialised knowledge in the field of computational science, while also being interdisciplinary in character. You will take courses alongside students from other specialisations, and there will be a chance to conduct joint projects and degree projects. You will take advanced courses to build on your subject knowledge in the natural sciences from your Bachelor’s degree.</p> <p>You will get general knowledge and skills of importance for computationally intensive professions, such as problem formulation, searching for information, data processing, scientific writing, and presentation techniques. The programme has strong ties with research. You will be taught by internationally well-recognised researchers, and you will be in contact with several research groups. The programme is taught in English.</p> <p><strong>Programme structure</strong><br /> You start by studying a course on greenhouse gases and biochemical cycles, followed by Mathematics for Scientists 2. The spring semester includes courses on ecosystem modelling, biostatistics and computational programming in Python.</p> <p>During the second year, you study courses on modelling in computational science, reproducible data analysis and statistical learning, an introduction to modelling climate systems and artificial neural networks and deep learning. The last semester is reserved for the Master’s thesis.</p> <p><strong>Career opportunities</strong><br /> Graduates of the programme can embark on several different career paths. The Master’s programme gives you a solid foundation for third-cycle education in the natural sciences, or a career within industry or business. You can thus also choose a career path outside academia and then find attractive jobs in areas where there is a need to solve natural science problems with the help of statistics, data processing or simulations, in industry or in public administration and other organisations.</p><p>https://www.maths.lu.se/english/education/masters-programme-in-applied-computational-science/</p>
Specialisation Physical Geography Bachelor´s degree of at least 180 credits, including 90 credits in science of which 15 credits should be in mathematics. Proficiency in English equivalent to English 6/B from Swedish upper-secondary school. Other information Some of the optional courses within the program may have higher requirements in mathematics or in other natural science subjects.
Varje tillfälle har egna datum och villkor. Avslutade tillfällen behålls som historik och innebär inte att en ny ansökan är öppen.
Lund University
Startdatum:
Slutdatum:
Studietakt: 100 %
Hämtad: .
Publicerad: .
Publiceringsversion: 8e217193-f5fa-4778-b085-a4521fd03e8d
Kontrollsumma: 1b0dc54c0fc8a359f83ba9dc8f9d468479ce432de4c03bce3b8b33dd67fe3f6c
Senast ändrad enligt källan: 2026-02-10T09:05:45