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DTSTART;VALUE=DATE:20260423
DTEND;VALUE=DATE:20260501
DTSTAMP:20260507T024138
CREATED:20260204T133730Z
LAST-MODIFIED:20260204T133730Z
UID:10001792-1776902400-1777593599@ddsa.dk
SUMMARY:Artificial intelligence for scientific writing
DESCRIPTION:This course aims to impart knowledge about and give participants an introduction to and practical experience using artificial intelligence (AI) tools to enhance their scientific writing processes.Disclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/artificial-intelligence-for-scientific-writing/
LOCATION:Aarhus
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260203
DTEND;VALUE=DATE:20260214
DTSTAMP:20260507T024138
CREATED:20260113T101350Z
LAST-MODIFIED:20260113T101350Z
UID:10001808-1770076800-1771027199@ddsa.dk
SUMMARY:Biostatistical modelling for Ag. Science
DESCRIPTION:Objectives of the course: \nIn the field of Agroecology\, rigorous data-driven research has become crucial for addressing complex agricultural and environmental challenges. The ability to effectively manage and analyse data is paramount for producing high-quality research outcomes and making informed decisions. \nMain course: Hands-on data: an applied statistics PhD course \nAim: Provide the participants with a place and the tools for methodological discussion around data analysis in the context of Agroecological research. The participants will finish the course being able to recognize the elements of experimental or observational designs involved in their own research as well as examples\, and model them to respond specific research questions. \nECTS: 3.5 One-week classes 90 hs. (40 hs. in class and 50 preparation). \nPre-workshop (necessary to complete 5 ECTS):  Handling Ag Data through R  \nAim: Reinforce the understanding and skill development on data handling and coding in R while revisiting the basic notions on descriptive statistics necessary to the course. \nECTS: 1.5 ECTS (48 hs.\, 3 day-long sessions) \nLearning outcomes and competencies: \nAt the end of the course\, the student should be able to: \n\nManage R statistical software that allows reshaping and merging data of various types.\n\n\nChoose and apply exploratory data tools to discover patterns in data.\n\n\nRecognise experimental designs and appropriately handle them statistically.\n\n\nEmploy common statistical methods (e.g.\, General Linear Mixed Models\, GLMM) for data analysis and understand their strengths and weaknesses.\n\n\nDescribe\, interpret and discuss the results and shortcomings of an analysis based on statistical modelling.\n\n\nBuild attractive and informative graphics and tables from applied statistical analysis.\nReport the results of an applied statistical analysis according to ethics of science.\n\nSpecifications: \nLanguage: English \nLevel of course: PhD course \nTime of year: 3 to 13 and 27 February 2026 \nNo. of contact hours/hours in total incl. preparation\, assignment(s) or the like: 125 \nCapacity limits: 20 \nWe present a complete course in data handling and analysis. The complete course encompasses a three-day pre-course on handling data through R and a week-long course on Biostatistics. Completed both courses provide a total of 5 ECTS\, and only attending the second week yields 3.5 ECTS. The course is planned for a maximum of 20 students. \nCourse contents: \nHands-on data: an applied statistics PhD course \n\nData handling and exploratory analysis\n\n\nData types and variables.\n\n\nDescriptive qualitative data analysis.\n\n\nDescriptive quantitative data analysis.\n\n\nIntroduction to statistical modelling\n\n\nNotation for statistical modelling.\n\n\nModelling experimental and observational data.\n\n\nStatistical modelling and uncertainty.\n\n\nModels for different types of covariates.\n\n\nExperimental design in agricultural science\n\n\nRandomization and Replication. Randomization restriction strategies.\n\n\nSingle-factor experiments. Experiments with factorial treatment structure. Crossed and nested factors. Number of required replications for desired power.\n\n\nExperiments with plot structures. Completely randomized designs\, blocked designs\, split-plot designs. Combining factorial treatment structures with plot structures.\n\n\nExperiment with temporal and spatial structures.\n\n\nType of sampling for observational studies.\n\n\nGeneral Linear Mixed Model\n\n\nLinear models. Simple and multiple linear regression. Estimation and confidence intervals. Hypothesis testing. Predicted values\, confidence bands\, and prediction intervals. Residual analysis. Model adequacy.\n\n\nRandom effect models. General concepts. Marginal models and subject-specific models. Models for residual covariance structure. Estimation of co-variances in normal populations. Inference on random effects. Best Linear Unbiased Predictor (BLUP). Goodness-of-fit criteria and model selection. Models for longitudinal data.\n\n\nResults and analysis communication \n\n\nGood practices in presenting results\n\n\nPrinciples of reproducible research (Open Data\, version control through Git and GitHub).\n\n\nDiscussion on the limitation of biostatistical modelling\n\nHandling Ag Data through R \n\nGetting started with R.\n\n\nIntroduction to the R working environment.\n\n\nWhat is R? What is RStudio? Download and installation of R and RStudio. Packages\, documentation\, and help in R.\n\n\nStarting a work session in R. Creating work projects in RStudio. First functions. R as a calculator. Language syntax. Statements. Assignments.\n\n\nMathematical operations. Comments. Saving commands (scripts) and projects.\n\n\nData files (data frame)\n\n\nReading data\, importing data from Excel and other formats\, reading files from a working directory. Exporting data\, saving objects\, and R workspace.\n\n\nHandling files and data\, sorting\, selecting rows\, selecting columns\, creating data subsets. Best practices for processing data and making the process reproducible.\n\n\nExploring data frames with specialized packages\, identifying missing values. Lists.\n\n\nGraphics\n\n\nThe plot() function. Scatter plots. Graphical attributes. Histogram. Box plot. Bar chart.\n\n\nIntroduction to Grammar of graphics and advance plotting using ggplot2 package.\n\n\nFunctions\n\n\nDefining functions in R. Function arguments. Writing code to create functions.\n\n\nFunctions from the apply family and loops.\n\n\nA refresh on descriptive statistics\n\n\nDescriptive statistics\n\n\nVariability in data. Distribution. Skew and kurtosis.\n\n\nDifferent measures of central tendency and when to use them.\n\n\nDifferent measures of variability and when to use them.\n\n\nStandardisation. Correlation.\n\n\nPopulations. Difference between describing a sample and statistical estimation.\n\n\nData model\n\n\nData model. Handling data collected from different sources with different methodologies.\n\n\nPrinciples of databases. Best practices for data management.\n\n\nPreparing a dataset for analysis.\n\nPrerequisites: Prerequisites: PhD students conducting quantitative data analysis as part of their research. \nCourse leader: René Gislum\, Associate Professor\, Head of Crop Health Section\, Department of Agroecology. \nName of lecturers: \nMaarit Mäenpää\, Doctoral degree in Evolutionary Ecology\, Master’s degree in Ecology\, Statistician in the Department of Agroecology. \nFranca Giannini-Kurina\, Doctoral degree in Agricultural Science\, Master’s degree in Applied Statistics. Postdoc at Soil Fertility Section\, Department of Agroecology. \nSimon Riley\, Doctoral degree in Agronomy\, Master’s degree in degree in Agronomy\, Statistician in the Department of Agroecology \nType of course/teaching methods: \nThis course attempts to differentiate itself from other postgraduate training programs in basics statistics by applying a student-centered pedagogical strategy where the teaching format includes lecturing and case-based teaching. The cases are based on PhD students’ own research problems and data to make the cases more relatable\, relevant\, and authentic\, and the cases are to be presented and discussed in-class activities. \nLiterature: \n\nCasler\, M. D. (2015). Fundamentals of experimental design: Guidelines for designing successful experiments. Agronomy Journal\, 107(2)\, 692-705.\nEfron\, B.\, & Hastie\, T. (2021). Computer age statistical inference\, student edition: algorithms\, evidence\, and data science (Vol. 6). Cambridge University Press.\nGlaz\, B.\, & Yeater\, K. M. (2020). Applied statistics in agricultural\, biological\, and environmental sciences (Vol. 172). John Wiley & Sons.\nPiepho\, H. P.\, Büchse\, A.\, & Emrich\, K. (2003). A hitchhiker’s guide to mixed models for randomized experiments. Journal of Agronomy and Crop Science\, 189(5)\, 310-322.\nPiepho\, H. P.\, Gabriel\, D.\, Hartung\, J.\, Büchse\, A.\, Grosse\, M.\, Kurz\, S.\, … & Wittenburg\, D. (2022). One\, two\, three: Portable sample size in agricultural research. The Journal of Agricultural Science\, 160(6)\, 459-482.\nKozak\, M.\, & Piepho\, H. P. (2018). What’s normal anyway? Residual plots are more telling than significance tests when checking ANOVA assumptions. Journal of agronomy and crop science\, 204(1)\, 86-98.\nWest\, B. T.\, Welch\, K. B.\, & Galecki\, A. T. (2022). Linear mixed models: a practical guide using statistical software. Crc Press.\nWickham\, H.\, Averick\, M.\, Bryan\, J.\, Chang\, W.\, McGowan\, L. D. A.\, François\, R.\, … & Yutani\, H. (2019). Welcome to the Tidyverse. Journal of open-source software\, 4(43)\, 1686.\nWickham\, H.\, & Grolemund\, G. (2017). R for data science (Vol. 2). Sebastopol: O’Reilly. https://r4ds.hadley.nz/\nWilkinson\, M. D.\, Dumontier\, M.\, Aalbersberg\, I. J.\, Appleton\, G.\, Axton\, M.\, Baak\, A.\, … & Mons\, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific data\, 3(1)\, 1-9.\n\nCourse assessment: \nIndividual student evaluation (pass/not pass) will be based on their own data analysis to be presented in a poster session. \nThe course will culminate in a collective assessment in an open-door session where participants will present the report of their own analysis. Students need to demonstrate the ability to effectively analyse\, interpret and communicate their results in the context of their research questions\, making their learning experience more meaningful and applicable to their future research endeavours. \nSpecial comments on this course: The course fee is 300 (incl. lunch and coffee). Participants are responsible for arranging their own accommodation and transportation to the campus. \nTime: February 3-13\, 2026\, from 9:00 to 17:00 \nPlace: Aarhus University Viborg\, Blichers Alle 20\, 8830 Tjele\, Denmark \nRegistration: \nThe deadline for registration is the 16 January 2026. Admission information will be sent shortly after. Please note the capacity limit (20 participants); the allotment will be based on a first-come\, first-served basis. \n\nFor registration: Biostatistical modelling for Ag. Science 2026 – Laravel\nIf you have any questions\, please contact Maarit Mäenpää\, e-mail: m.maenpaa@agro.au.dk\n\nThat the assessment date is the 27 of February\, it is mandatory to participate either in person or online. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/biostatistical-modelling-for-ag-science/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250407
DTEND;VALUE=DATE:20250412
DTSTAMP:20260507T024138
CREATED:20250305T135546Z
LAST-MODIFIED:20250305T135546Z
UID:10001565-1743984000-1744415999@ddsa.dk
SUMMARY:Bayesian Hierarchical Modelling 2025
DESCRIPTION:Objectives of the course:\nThe PhD students will be introduced to Bayesian hierarchical modelling\, which are becoming increasingly popular for fitting ecological\, environmental\, and human disease models to temporal and spatial data. The aim of the course is to introduce the students to i) the applied use of likelihood functions and Bayesian statistics\, ii) setting up advanced hierarchical statistical models with latent variables\, iii) applying advanced statistical models\, and iv) making quantitative predictions with a known degree of uncertainty. \nLearning outcomes and competences: \nAt the end of the course\, the student should be able to: \n– assess the possible value of using advanced hierarchical statistical methods in the students own work \n– critically evaluate scientific literature using advanced statistical models \nCourse parameters: \nLanguage: English \nLevel of course: PhD course \nTime of year: Spring 2025 \nNo. of contact hours/hours in total incl. preparation\, assignment or the like: 35/80 \nCapacity limits: 16 participants \nCompulsory program: preparation\, active participation\, assignment \nCourse contents: \n\nIntroduction to likelihood functions and Bayesian statistics\nHierarchical models with latent variables\nFitting models to data using Bayesian methods\nModel prediction\n\nPrerequisites: Introductory probability and statistics courses \nName of lecturers: Christian Damgaard and Peter Borgen Sørensen \nType of course/teaching methods: Seminars and exercises using R \nLiterature: Before course start the student are expected to have read chapters 1\, 3-7 in the electronic book: https://bayesball.github.io/BOOK/probability-a-measurement-of-uncertainty.html\, and be familiar with the statistic software R (e.g. http://r.sund.ku.dk/) \nWe will use the software “RTMB” at the course – https://cran.rproject.org/web/packages/RTMB/vignettes/RTMB-introduction.html \nSoftware: R\, RStudio\, RTMB \nCourse assessment: Personalized reports (approximately 10-30 pages\, corresponding to a workload of 20 hours outside\, and in the week after the end of the scheduled classes) must be completed and submitted for approval (pass/fail). \nSpecial comments on this course: All expenses for accommodation and travel are paid by the student. \nTime:  7-11 April 2025 \nPlace: Department of Ecoscience\, Aarhus University\, Denmark \nRegistration: Deadline for registration is 1 April 2025 (first come\, first served). \nFor registration: Please write an e-mail to Christian Damgaard\, e-mail: cfd@ecos.au.dk \nCourse Program \nThe topics of the 5 days are as detailed below\, and each topic starts with a lecture followed by computer exercises in R which are carried out in teams of two-three participants. Each participant must produce a personalized report of the exercises. During the course\, the participants should be prepared to work outside the scheduled classes to complete the computer exercises. \nDay 1 \n10:00 – 10:15                        Welcome\, Introduction to Course \n10:00 – 12:00                        Lecture 1: Probability theory – the logic of science \n12:00 – 13:00                        Lunch \n13:00 – 15:00                        Lecture 2: Probability distributions and likelihood functions\, exercises in R \n15:00 – 15:15                        Break \n15:15 – 16:00                        Short plenum presentation of the student’s own data and methods. \nDay 2 \n08:30 – 10:00                        Lecture 3: Bayesian statistics and MCMC\, exercises in R \n10:00 – 10:15                        Break \n10:15 – 12:00                        Lecture 4: Laplace’s approximation – RTMB \n12:00 – 13:00                        Lunch \n13:00 – 15:00                        Exercises in RTMB \n15:00 – 15:15                        Break \n15:15 – 16:00                        Exercises in RTMB \nDay 3 \n08:30 – 10:00                        Lecture 5: Structural equation modelling \n10:00 – 10:15                        Break \n10:15 – 12:00                        Exercises in RTMB \n12:00 – 13:00                        Lunch \n13:00 – 15:00                        Exercises in RTMB \n15:00 – 15:15                        Break \n15:15 – 16:00                        Exercises in RTMB \nDay 4 \n08:30 – 10:00                        Lecture 6: Prediction and uncertainties \n10:00 – 10:15                        Break \n10:15 – 12:00                        Exercises in RTMB \n12:00 – 13:00                        Lunch \n13:00 – 15:00                        Exercises in RTMB \n15:00 – 15:15                        Break \n15:15 – 16:00                        Exercises in RTMB \nDay 5 \n08:30 – 10:00                        Exercises in RTMB \n10:00 – 10:15                        Break \n10:15 – 12:00                        Evaluation in plenum to identify relevant methods for students’ own data. \n12:00 – 13:00                        Lunch \n13:00 – 14:00                        Evaluation and departure \nWithin two weeks                   Submission of final report by e-mail to Christian Damgaard \nIf you have any questions\, please contact Christian Damgaard or Peter Borgen Sørensen. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/bayesian-hierarchical-modelling-2025/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20241210
DTEND;VALUE=DATE:20241213
DTSTAMP:20260507T024138
CREATED:20240821T085620Z
LAST-MODIFIED:20240821T085620Z
UID:10001333-1733788800-1734047999@ddsa.dk
SUMMARY:Generalized additive modelling with R
DESCRIPTION:Objectives of the course: \nThe course will provide an applied introduction to generalized additive modelling in R for biologists. Most of the statistical methods you are likely to have encountered will have specified fixed functional forms for the relationships between covariates and the response\, either implicitly or explicitly. These might be linear effects or involve polynomials\, such as x + x2 + x3. Generalized additive models (GAMs) are different; they build upon the generalized linear model by allowing the shapes of the relationships between response and covariates to be learned from the data using splines. Modern GAMs are a general data analysis framework\, encompassing many models as special cases\, including GLMs and GLMMs\, and the variety of splines available to users allows GAMs to be used in surprisingly large situations. In this course we’ll show you how to leverage the power and flexibility of splines to go beyond parametric modelling techniques like GLMs. \n Learning outcomes and competences:\n At the end of the course\, the student should be able to: \n\nUnderstand how GAMs work from a practical viewpoint to learn relationships between covariates and response from the data\nBe able to fit GAMs in R using the mgcv package\nKnow the differences between the types of splines and when to use them in your models\nKnow how to visualise fitted GAMs and to check the assumptions of the model\nknow how to test specific hypotheses and estimate quantities of interest using fitted models\,\nbe able to use the R statistical software and in particular the mgcv\, gratia\, and marginaleffects packages to fit and analyse generalized additive models.\n\n  \nCourse parameters: \nLanguage: English \nLevel of course: PhD Course \nTime of year: Autumn 2024 (10 – 12 December 2024) \nCapacity limits: 30 \nCourse fee: DKK 350 \n Compulsory programme: \nActive participation in the course including attendance at lectures and completion of computer-based classes and exercises. Completion of short\, computer-based assessments testing their understanding of a topic and the practical skills taught. For credit\, students must complete a data analysis exercise to be submitted one week after the end of the course (19 December). \n Course contents: \nThe course is based on a series of lectures and computer-based practical classes led by an international expert in generalized additive modelling and who is the author of gratia\, an R package for working with GAMs fitted using the mgcv package. \nThe course covers the following topics: \n\nA recap of generalized linear models for data that are not Gaussian\nFitting GAMs using mgcv\nWorking with penalized splines to estimate flexible effects of covariates\nModel diagnostics and assessment\nEstimating marginal effects and adjusted predictions with GAMs\nHypothesis testing using GAMs\nDisplaying model estimates and reporting results\n\n Prerequisites: \nThis course is suitable for Phd students (including senior thesis-based MSc students) and researchers working with biological data who want to fit models that allow for nonlinear relationships (effects) of covariates on responses. The course will be of particular interest to PhD candidates and researchers in inter alia biology\, animal science\, ecology\, agriculture\, and environmental science. Some prior knowledge of R is required\, and some prior knowledge of generalized linear modelling in R would be an advantage. \n  \n Literature: \nOpen access teaching resources prepared by the course leader will be supplemented by original literature (papers). Electronic copies of the open access teaching resources will be provided to each participant before the course starts. \n Course homepage: \nhttps://github.com/gavinsimpson/au-viborg-gam-course \n Course assessment: \nThe course will be assessed through a data analysis exercise (take home) to be submitted by 19 December 2024. \n\n Time: 3 days of teaching in a single block (10 – 12 December 2024). Classes are held from 09.30 to 16.00 each day.\n Place: The course will be taught at AU Campus Viborg\n Course fee: DKK 350 \n Registration: \nPlease send an e-mail to Julie Jensen\, e-mail jsj@anivet.au.dk no later than 6 December 2024 to register. \nIf you have any questions\, please contact course leader Assistant Professor Gavin Simpson\, Department of Animal and Veterinary Sciences\, Aarhus University\, e-mail: gavin@anivet.au.dk \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/generalized-additive-modelling-with-r/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20241022
DTEND;VALUE=DATE:20241024
DTSTAMP:20260507T024138
CREATED:20240424T103039Z
LAST-MODIFIED:20240424T103039Z
UID:10001102-1729555200-1729727999@ddsa.dk
SUMMARY:Introduction to R
DESCRIPTION:The aim of the course is to introduce the student to the basic use of the software R. The course is designed to build up the basic skills in R necessary for attending to the PhD course Basic Statistical Analysis.\n         Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/introduction-to-r/
LOCATION:Aarhus
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20241009
DTEND;VALUE=DATE:20241024
DTSTAMP:20260507T024138
CREATED:20240424T090734Z
LAST-MODIFIED:20240424T090734Z
UID:10001103-1728432000-1729727999@ddsa.dk
SUMMARY:Basic Data Science in Python
DESCRIPTION:The aim of the course is to introduce the PhD student to basic tasks\, methods and evaluation procedures in data science\, using Python and its libraries and environments.\n         Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/basic-data-science-in-python/
LOCATION:Aarhus
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240930
DTEND;VALUE=DATE:20241005
DTSTAMP:20260507T024138
CREATED:20240424T084721Z
LAST-MODIFIED:20240424T084721Z
UID:10001183-1727654400-1728086399@ddsa.dk
SUMMARY:Aarhus Comprehensive Computational Entomology Summer School (ACCESS) 2024
DESCRIPTION:Objectives of the course:\n This course is aimed at a growing cohort of postgraduate data scientists and biologists who use automated methods to study insects (and other arthropods) in their research. Participants will be introduced to the state-of-the-art when it comes to automated monitoring of insects\, including various modes of image acquisition\, their strengths and weaknesses\, approaches to image annotation and data management\, and models to automatically extract biological information from sensor-derived media such as images and sounds. \nLearning outcomes and competences:\n At the end of the course\, the student should be able to: \n\nPlan and communicate a project using automated monitoring to address important questions in entomology.\nSelect appropriate automated monitoring methods based on strengths\, weaknesses\, and data requirements.\nDesign a data acquisition program using autonomous sensors\, especially cameras\, with special attention to hardware design\, deployment\, and scheduling.\nGenerate labeled datasets for analysis or model training using strategic manual annotation.\nManage data appropriately with reference to metadata standards.\nTrain models to detect\, classify or characterize insects in images.\nDeploy open-source models to achieve research goals\, with careful consideration of domain shifts and model uncertainty.\n\nCourse parameters: Language: English \nLevel of course: Postgraduate \nTime of year: Q3 2024 \nNo. of contact hours/hours in total incl. preparation\, assignment(s) or the like: 37/67 \nCapacity limits: 20 participants \nCourse fee: No fee\, applications competitive \nCompulsory programme: \nCourse contents:\n Participants will achieve their learning outcomes through a combination of case-based learning\, delivered in the form of guest lectures and workshops from international experts\, and project-based learning with a focus on presentation and interaction with peers. Participants will be strategically arranged into teams (based on complementary interests) before the summer school and will carry out up to 30h of preparation before they travel to the venue. During the summer school they will co-develop research projects\, which they will iteratively present to their peers and receive constructive feedback. Finally\, participants’ experiences will be grounded in reality through multiple hardware demonstrations\, especially under field conditions\, in the Mols Bjerge National Park. \nPrerequisites:\n None \nName of lecturers:\n Jamie Alison\, Quentin Geissmann\, Toke Høye\, Jarrett Blair\, Charlie Outhwaite\, James Crall\, Hjalte Mann\, Jenna Lawson\, Luca Pegoraro \nType of course/teaching methods:\n Lectures\, workshops\, field demonstrations\, group work \nLiterature:\n None \nCourse assessment:\n Success is determined based on a final project proposal document and a presentation of the proposal \nSpecial comments on this course:\n The course provides free accommodation and food for the selected candidates\, but travel to and from the venue must be covered by the participants or their institutions. In 2024 the course is funded by the Danish Ministry of Higher Education and Science though a Global Innovation Network Program grant on Automated Monitoring of Insects. \nTime:\n 30 September to 4 October 2024 \nPlace:\n Mols laboratory research station\, near Aarhus\, Denmark \nCourse fee:\n No fee\, applications competetive \nRegistration:\n To complete your application\, please fill out the application form by 15 April 2024. \nIf you have any questions\, please contact Quentin Geissmann: qgeissmann@qgg.au.dk \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/aarhus-comprehensive-computational-entomology-summer-school-access-2024/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240923
DTEND;VALUE=DATE:20240928
DTSTAMP:20260507T024138
CREATED:20240821T090238Z
LAST-MODIFIED:20240821T090238Z
UID:10001332-1727049600-1727481599@ddsa.dk
SUMMARY:Modelling production and environmental impacts of cropping and grassland systems using APSIM (2024)
DESCRIPTION:Objectives of the course: \nThe course aims to give insight into the basic function of process-based ecosystem models applied to cropping and grassland systems (commonly known as crop models). The course will have special focus on biophysical modelling using the Agricultural Production Systems Model (APSIM). \n  \nLearning outcomes and competences: \nAt the end of the course\, the student should be able to: \n• set up an APSIM simulation for different environments (soil types\, climates) and different cropping systems and management options\, using appropriate input parameters and initial conditions. \n• process model outputs and test these based on experimental data using simple statistics. \n• have a general understanding of different modelling approaches and limitations and benefits. \n  \nCourse parameters: \nLanguage: English \nLevel of course: PhD course \nTime of year: September 2024 \nNo. of contact hours/hours in total incl. preparation\, assignment(s) or the like: \nFor the ETCS course: 40h of lectures distributed in 5 lecture days; 20h for working in homework/simulation exercises and self-study during the course\, 55 h familiarizing with relevant literature\, report writing and preparation of a presentation of simulation study on one of the topics covered during the course\, 10 h presentations and discussions with course participants\, plus oral examination. The total workload is 125h. \nFor the additional 2.5 ETCS: 40 h APSIM model on own project with consultations with the instructor(s) and oral examination; 25 h for writing a report. The additional workload is 65h. \nCourse fee: The course fee is DKK 4500/EUR 600. \n  \nCompulsory programme: \nactive participation\, assignments\, oral presentation\, and examination\, report (for additional 2.5 ETCS) \n  \nCourse contents: \nThis course provides a general introduction to crop models: the basic principles and approaches behind modelling plant growth\, soil hydrology\, soil biogeochemistry and near-ground atmospheric interactions. The course further offers an introduction to the biophysical Agricultural Production Systems Model (APSIM). This is done through a series of short lectures by the instructors\, which explain the science behind the various sub-models in APSIM\, namely water flow\, solute transport (focusing on nitrogen)\, soil organic matter turnover\, transfer of water and energy in the soil-plant-atmosphere continuum\, and crop (production) model. Each of these will be followed by hands-on exercises where the participants learn how to use APSIM for a simple\, pre-defined system\, about the required data inputs\, and model parameter initialization. \nThe course will focus on Northern European production systems that include wheat\, maize\, pulses\, and cover crop rotations\, and address aspects such as plant growth and development\, crop yield response to management practices (e.g. planting date\, cultivar\, N rate)\, crop rotations\, soil water processes (e.g. drainage\, evaporation)\, soil carbon\, nitrogen and surface organic matter dynamics (e.g. N mineralization and residue decomposition). \nFurthermore\, the course will address data analysis\, and how to extract and analyse model output data for both model calibration\, testing and scenario analysis. For this\, various statistical approaches will be discussed. An overview of different models regarding complexity\, data requirement\, accuracy\, and transferability will also be presented and discussed. \n  \nPrerequisites: \nThe PhD student must master data analysis\, and preferably have some knowledge in programming. \nThe course will use the APSIM model (http://www.apsim.info). It will be assumed that the PhD students have the software installed on their computers. \n  \nName of lecturers: \n• Iris Vogeler\, Senior Researcher. Department of Agroecology\, Aarhus University. \nResponsible for overview on modelling and overall course coordination. \n• Val Snow\, Principal Scientist\, AgResearch\, New Zealand. Responsible for theory and modelling approaches in nitrogen cycling and movement & soil organic matter dynamics. \n• Davide Cammarano\, Professor. Department of Agroecology\, Aarhus University. Responsible for introduction into environmental modelling approaches. \n• Jorge F. Miranda Vélez\, PostDoc. Department of Agroecology\, Aarhus University\, Responsible for water dynamics in soils. \n• Uttam Kumar\, Postdoc. Department of Agroecology\, Aarhus University. Responsible for simulation of arable crops. \n• Maarit Mäenpää\, Academic employee. Department of Agroecology\, Aarhus University. Responsible for data analysis and statistics. \n  \nType of course/teaching methods: \nLectures alternated with supervised exercises and self-study including the elaboration of a report on the self-chosen project. The course responsible will offer feedback to a draft of the reports\, provided the draft is delivered before a deadline established at the beginning of the course by the course responsible. \n  \nCourse assessment: \nClasswork – satisfactory participation in the course; Group work oral presentation and examination. Full attendance to the lectures is a necessary condition to participation in the oral examination. For the additional 2.5 ETCS a report on a self-chosen project. \n  \nTime: 23 -27 September 2024 \nPlace: AU Viborg – Department of Agroecology\, Aarhus University\, Blichers Allé 20\, Postboks 50\, DK-8830 Tjele \nCourse fee: DKK 4500 DKK/EUR 600. \nECTS \n5 or 7.5 \n  \n  \nRegistration: \nThe deadline for registration is 31 July. Admission information will be sent out no later than 15 August 2024. \nIf you have any questions\, please contact Iris Vogeler\, e-mail: iris.vogeler@agro.au.dk \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/modelling-production-and-environmental-impacts-of-cropping-and-grassland-systems-using-apsim-2024/
CATEGORIES:PhD Course
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DTSTART;VALUE=DATE:20240909
DTEND;VALUE=DATE:20240914
DTSTAMP:20260507T024138
CREATED:20240424T091241Z
LAST-MODIFIED:20240424T091241Z
UID:10001101-1725840000-1726271999@ddsa.dk
SUMMARY:Introduction to Python for Data Science
DESCRIPTION:The aim of the course is to introduce the student to the basic use of the programming language Python. The course is designed to build up the basic skills in Python necessary for attending the course Basic Data Science in Python.\n         Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/introduction-to-python-for-data-science/
LOCATION:Aarhus
CATEGORIES:PhD Course
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20240610
DTEND;VALUE=DATE:20240612
DTSTAMP:20260507T024138
CREATED:20240424T084259Z
LAST-MODIFIED:20240424T084259Z
UID:10001177-1717977600-1718150399@ddsa.dk
SUMMARY:Dairy protein biochemistry and proteomics 2024
DESCRIPTION:Objectives of the course: \nThe course is intended as a combined PhD course and Masterclass. Dairy proteins are an important component of dairy products\, but also one of the most widely used proteins in the food industry. Within this masterclass\, you will learn about the specific features of the two main classes of dairy proteins\, whey proteins and caseins. You will gain insight into the different analytical methodologies that exist to characterize dairy proteins\, their variations\, and molecular features\, like modifications. Finally\, you will gain quantitative insight into the nutritional quality of dairy proteins in the human diet. Research communication aspects in the context of your project and the discussion of application of analytical methods included at the course. \n  \nLearning outcomes and competences: \nAt the end of the course\, the student should be able to: \n– Use original scientific literature in the design and planning of experiments related to milk protein biochemistry \n– Critically assess obtained results regarding milk protein biochemical features using protein chemistry techniques \n– Account for relations between structural features in milk proteins and their technological and nutritious properties \n-Being able to assess choice of methodologies for assessment of milk protein biochemistry \n-Understand the concepts of proteomics\, peptidomics and methodologies behind\, including examples of relevant bioinformatics tools to support these \n-Communicate own research \n  \nCourse parameters: \nLanguage: English \nLevel of course: PhD course \nTime of year: 10-11 June 2024 \nNo. of contact hours/hours in total incl. preparation\, assignment(s) or the like: 18/58 \nCapacity limits: 30 \nCourse fee: VLAG and PhD students at Danish universities DKK 1000\, other PhD students and post docs DKK 1500\, professionals and non-academics DKK 2500. \n  \nCompulsory programme: \nPrepare for the course by reading the literature handed out prior to the course \nPrepare a one-slide presentation of yourself and send in prior to the course \nPrepare a poster of your project or work and send in prior to the course \nPresent your poster at the course and discuss your project or work in the context of the course content\, with special emphasis on potential application of methodologies covered by the course \nParticipate in the 2 d program (own presentations +lectures + group work). \n  \nCourse contents: \nPresentation of yourself\, presentation\, and discussion of your PhD project/own work. Lectures. Presentations and group work/discussions on theoretical assignments (incl. e.g. discussion of different models of casein micelles\, interpretation of analytical data and outputs\, discussions of original research papers relating to methodologies and milk protein biochemistry\, questions related to impact of processing on milk protein biochemistry\, use of bioinformatics). \n  \nPrerequisites: \nPhD student (or ask course responsible if questions) or professional (with at least MSc) in industry (Masterclass) working within the area. \nThe course is aimed at research professionals (PhD level)\, who already have basic knowledge about protein (bio)chemistry and want to increase their specific knowledge about dairy proteins from a physico-chemical\, analytical\, and nutritional perspective. Participants should have a background in food/nutritional science\, biology\, chemistry or other life sciences. \n  \nName of lecturers: \nProfessor Lotte Bach Larsen\, Department of Food Science\, AU \nAssociate professor Nina Aagaard Poulsen\, Department of Food Science\, AU \nAssociate professor Kasper Hettinga\, Food Quality and Design\, WUR \nAssistant professor Etske Bijl\, Food Quality and Design\, WUR \n  \nType of course/teaching methods: \nOwn presentations\, lectures\, group work \n  \nLiterature: \nOriginal scientific literature\, reviews and book chapters will be handed out to the students in advance of the course. \n  \nCourse homepage: \nhttps://events.au.dk/dairyproteinphd \nBrightspace will be established for course participants. \n  \nCourse assessment: \nParticipation in the program and in the group work. A course certificate will be provided based on the participation. \n  \nSpecial comments on this course: \nThe PhD course/Masterclass is a joint course between AU and WUR. The aim is to hold it every 1-2 years\, alternating between WUR and AU. The course was held for the first time at WUR in the summer of 2018. \n  \nTime: \n10-11 June 2024 \n  \nPlace: \nAarhus\, Denmark \n  \nCourse fee: \nVLAG and PhD students at Danish universities DKK 1000\, other PhD students and post docs DKK 1500\, professionals and non-academics DKK 2500. \n  \nRegistration: \nDeadline for registration is 10 May 2024. Information regarding admission will be sent out no later than 24 May 2024. \nPlease register as early as possible via https://events.au.dk/dairyproteinphd \n\n If you have any questions\, please contact Lotte Bach Larsen\, lbl@food.au.dk or Nina Aagaard Poulsen\, nina.poulsen@food.au.dk. \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/dairy-protein-biochemistry-and-proteomics-2024/
LOCATION:Aarhus
CATEGORIES:PhD Course
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