{"id":190,"date":"2026-05-07T09:59:16","date_gmt":"2026-05-07T06:59:16","guid":{"rendered":"https:\/\/sites.uef.fi\/dp-fobi\/?page_id=190"},"modified":"2026-06-10T15:16:22","modified_gmt":"2026-06-10T12:16:22","slug":"coupling-artificial-intelligence","status":"publish","type":"page","link":"https:\/\/sites.uef.fi\/dp-fobi\/coupling-artificial-intelligence\/","title":{"rendered":"Coupling artificial intelligence and simulation-optimization for numeric forest management planning"},"content":{"rendered":"<p><a href=\"https:\/\/uef.varbi.com\/en\/what:job\/jobID:944659\/\"><button>APPLY FOR THIS PhD POSITION <\/button><\/a> <strong>Application period: 11 June \u2013 10 August, 2026<\/strong><\/p>\n\n\n<h2 class=\"wp-block-heading\">Research project description<\/h2>\n\n\n\n<p>This research project analyzes forest inventory data from two time points, approximately 10 years apart, to detect and characterize different harvesting practices, such as clearcuts and thinning, in a large forest landscape of about 200,000 hectares. Simultaneously, this research uses conventional simulation based approaches to project forest development resulting from various management alternatives. Using this database, the research compares the simulated alternatives to the realized management to determine the management regime of the numerous forest owners in the landscape. The research applies deep learning and generative AI approaches to identify spatial drivers of forest management and predict natural processes and harvesting.<\/p>\n\n\n\n<p><strong>The main scientific objective is to advance the understanding of how socioeconomic, demographic, and biophysical factors interact to shape timber harvesting patterns from the scale of individual forest holdings to regions. <\/strong>Ultimately the aim is to better capture the forest owners\u2019 decision-making aspects and ensure that our predictive scenarios reflect real-world preferences and constraints.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Academic background and skills of the applicant<\/h2>\n\n\n\n<p>An ideal candidate holds a <strong>master&#8217;s degree in forestry or data science, computer science, applied mathematics, statistics, geoinformation sciences, or a closely related field<\/strong>. The candidate is expected to have experience in simulation and optimization systems for forest planning or natural resources planning in general; and demonstrated numeric skills and ability to implement computations (preferably in a programming or scripting environment such as R). Earlier hands-on experience with deep learning and generative AI methods is an asset but not necessity. The person should have studies from a scientific field relevant for the role described above and the ability to work in international and multidisciplinary research teams, demonstrated scientific writing, communication and presentation capabilities, and an ability to work independently. The selected candidate must fulfil the language skills requirements of the Doctoral Programme in Science, Forestry and Technology at UEF (see <a href=\"https:\/\/www.uef.fi\/en\/degree-programme\/doctoral-programme-in-science-forestry-and-technology\">https:\/\/www.uef.fi\/en\/degree-programme\/doctoral-programme-in-science-forestry-and-technology<\/a> (Eligibility and admission criteria)).<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Doctoral programme and research group<\/h2>\n\n\n\n<p>Doctoral education in the University of Eastern Finland is arranged in seven discipline specific or thematic doctoral programmes. This research project will be located in the <a href=\"https:\/\/www.uef.fi\/en\/degree-programme\/doctoral-programme-in-science-forestry-and-technology\">Doctoral Programme in Science, Forestry and Technology<\/a>, and the submitting department is the <a href=\"https:\/\/www.uef.fi\/en\/unit\/school-of-forest-sciences\">School of Forest Sciences<\/a>.<\/p>\n\n\n\n<p>The doctoral candidate will join the research group of forest planning led by Professor Jari Vauhkonen. This research group is a multidisciplinary group focused on the use of forest inventory data to support multi-objective forest planning and decision analysis. The group\u2019s research has strong emphases on advanced data analytics and modeling techniques, understanding of social and behavioral dimensions, and the heterogeneity of forest owners in the national and international forest policy context, which are studied in a number of recent research projects funded by the Research Council of Finland for example.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Partners \/ Secondments<\/h2>\n\n\n\n<p>Swedish University of Agricultural Sciences, Department of Forest Resource Management, hosted by Prof. Karin \u00d6hman<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Other interdisciplinary, international and\/or intersectoral collaboration<\/strong><\/h2>\n\n\n\n<p>Intersectoral and interdisciplinary collaboration is strengthened through DP-FOBI&#8217;s three summer schools involving academic supervisors, non-academic partners, and other stakeholders, enabling hands-on interaction and knowledge exchange.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Supervisors and related research<\/h2>\n\n\n\t<div id=\"accordion-block_a1b39af60247f7307043af46d9598163\" class=\"accordions\">\n\t\t\t\t\t<div class=\"accordion accordion-js\">\n\t\t\t\t<button class=\"accordion__button\" aria-controls=\"content-4524\" aria-expanded=\"false\" id=\"accordion-control-4524\">\n\t\t\t\t\t<h3 class=\"accordion__heading\" >\n\t\t\t\t\t\tJari Vauhkonen\t\t\t\t\t<\/h3>\n\t\t\t\t<\/button>\n\t\t\t\t<div class=\"accordion__content\" role=\"region\" aria-labelledby=\"accordion-control-4524\" aria-hidden=\"true\" id=\"content-4524\">\n\t\t\t\t\t<p>Dr. <a href=\"https:\/\/uefconnect.uef.fi\/en\/jari.vauhkonen\/\">Jari Vauhkonen<\/a> is a professor of forest planning and an adjunct professor of forest remote sensing at the UEF. He will be the main supervisor of this proposed topic. His research focus was earlier related to remote sensing, but it has broadened to the use of forest inventory data to support multi-objective forest planning and decision analysis. He is especially interested in how artificial intelligence, deep machine learning and similar techniques can be applied to augment conventional modelling. Jari Vauhkonen has been a supervisor of 4 on-going and 3 completed PhD theses and 16 completed MSc theses.<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t\t\t<div class=\"accordion accordion-js\">\n\t\t\t\t<button class=\"accordion__button\" aria-controls=\"content-6644\" aria-expanded=\"false\" id=\"accordion-control-6644\">\n\t\t\t\t\t<h3 class=\"accordion__heading\" >\n\t\t\t\t\t\tTimo Tokola\t\t\t\t\t<\/h3>\n\t\t\t\t<\/button>\n\t\t\t\t<div class=\"accordion__content\" role=\"region\" aria-labelledby=\"accordion-control-6644\" aria-hidden=\"true\" id=\"content-6644\">\n\t\t\t\t\t<p>Dr. <a href=\"https:\/\/uefconnect.uef.fi\/en\/timo.tokola\/\">Timo Tokola<\/a> is a professor of forest information systems at the UEF. His expertise areas include remote sensing, Geographical Information Systems (GIS), information system design and management in natural resource inventory, forest management planning and decision making. He has been a research coordinator of various research projects and overall has broad experience in leading various national and international academic programmes and projects.\u00a0Timo Tokola has been a supervisor of 21 completed PhD theses and 46 completed MSc theses.<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t\t\t<div class=\"accordion accordion-js\">\n\t\t\t\t<button class=\"accordion__button\" aria-controls=\"content-8853\" aria-expanded=\"false\" id=\"accordion-control-8853\">\n\t\t\t\t\t<h3 class=\"accordion__heading\" >\n\t\t\t\t\t\tRelated articles\t\t\t\t\t<\/h3>\n\t\t\t\t<\/button>\n\t\t\t\t<div class=\"accordion__content\" role=\"region\" aria-labelledby=\"accordion-control-8853\" aria-hidden=\"true\" id=\"content-8853\">\n\t\t\t\t\t<p>Hamedianfar, A., Mohamedou, C., Kangas, A., &amp; Vauhkonen, J. (2022). Deep learning for forest inventory and planning: a critical review on the remote sensing approaches so far and prospects for further applications. Forestry, 95(4), 451-465.<\/p>\n<p>Mohamedou, C., Korhonen, L., Eerik\u00e4inen, K., &amp; Tokola, T. (2019). Using LiDAR-modified topographic wetness index, terrain attributes with leaf area index to improve a single-tree growth model in south-eastern Finland. Forestry, 92(3), 253-263.<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t<\/div>\n\t","protected":false},"excerpt":{"rendered":"<p>APPLY FOR THIS PhD POSITION Application period: 11 June \u2013 10 August, 2026 Research project description This research project analyzes forest inventory data from two time points, approximately 10 years apart, to detect and characterize different harvesting practices, such as clearcuts and thinning, in a large forest landscape of about 200,000 hectares. Simultaneously, this research [&hellip;]<\/p>\n","protected":false},"author":1364,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-190","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Coupling artificial intelligence and simulation-optimization for numeric forest management planning - Doctoral Programme for Advancing Forest-Based Bioeconomy (DP-FOBI)<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sites.uef.fi\/dp-fobi\/coupling-artificial-intelligence\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Coupling artificial intelligence and simulation-optimization for numeric forest management planning - Doctoral Programme for Advancing Forest-Based Bioeconomy (DP-FOBI)\" \/>\n<meta property=\"og:description\" content=\"APPLY FOR THIS PhD POSITION Application period: 11 June \u2013 10 August, 2026 Research project description This research project analyzes forest inventory data from two time points, approximately 10 years apart, to detect and characterize different harvesting practices, such as clearcuts and thinning, in a large forest landscape of about 200,000 hectares. 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