ACIT4830 Special Robotics and Control Subject Emneplan

The course provides an arena where students can learn about specific technologies and methods that are relevant for applications in robotics and control. These themes can be varied from artificial intelligence methods for robotics and control, Internet of Things and sensor network systems, autonomous and distributed systems, embedded systems, industrial process control, and other special subjects within robotics and control.

The first part of the course is organised as a series of lectures and seminars. The second part of the course is a practical project. The course is completed by the students submitting a report and giving a presentation of their work.

The assessment/examination is based on a group porfolio consisting of:

• a written project report (between 5.000-10.000 words)
• an oral presentation and discussion (20 + 10 minutes)

The portfolio is assessed as a whole and cannot be appealed.

No formal requirements over and above the admission requirements.

A student who has completed this course should have the following learning outcomes defined in terms of knowledge, skills and general competenc.

Knowledge

On successful completion of this course the student has:

• advanced knowledge within a sub-area of robotics and control.
• knowledge about the process of planning and conducting a project.

Skills

On successful completion of this course the student can:

• apply the theoretical knowledge and research-based methodologies into a practical problem.
• propose a detailed project plan.
• write a scientific report.

General competence

On successful completion of this course the student can:

• analyze, present and debate specific research subjects in light of the theoretical and practical approaches.
• discuss the subject both at expert and non-expert levels.

The first part of the course is organised into a series of lectures and seminars. Students are expected to play an active role. Lectures are given by the course lecturer and invited lecturers. Students will also be required to present papers, and discuss course themes during lectures and seminars.

The second part of the course is a practical project in groups of 1-3 students. The course is completed by the students submitting a report and giving a presentation of their work.

An interdisciplinary group project designed to develop students' project management skills, co-operation, communication skills, individual creativity and ability to apply their knowledge and competencies in different contexts.

The course will introduce students to basic bioinformatics including best practice when setting up and managing bioinformatics projects. The course covers introduction to high throughput sequencing technologies and will give students hands-on experience with the analysis of data from various sequencing platforms. Applications that are included in the practical part are processing of raw data reads, control of quantity and quality of data (FASTQC), expression analysis of small RNA sequencing data (miRNA) and transcriptome sequencing (mRNA-seq) data, genomic DNA sequencing. Some post-analytical tools like gene ontology enrichment analysis and clustering/heat-maps of differentially expressed genes are introduced to students and included in the hands-on exercises.

This course is primarily aimed at PhD candidates admitted to the PhD programme in Health Sciences. General terms for admission to the course is a completed master’s degree in molecular biology or equivalent qualification (e.g. completed MABIO4410). Priority will be given to PhD candidates from OsloMet.

Note that all students must have a laptop not more than 2 years old (mac is easiest, but windows with linux also works). The laptop must be able to connect to a wireless network, and the student must have administrative rights to download and install software and bioinformatic packages.

The course can also be offered to students who have been admitted to the "Health Science Research Programme, 60 ECTS", by prior approval from the supervisor and based on given guidelines for the research programme.

Admission to the course may also be offered to master students attending the Master’s Programme in Biomedicine / Master in Health and Technology, by prior approval from the thesis supervisor and depending on course capacity. These students must have completed the first 60 ECTS credits of the Master’s programme including MABIO4410, and write a master’s project where this course is considered relevant.

After completing the course, the student should have the following overall learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

Upon successful completion of the course, the student knows:

• how to collaborate and study in complex teams
• how to employ project management methodologies when planning and executing a project
• how to apply scientific knowledge and processes to solve multi-disciplinary problems, and methods for analysing outcomes

Skills

Upon successful completion of the course, the student can:

• collaborate effectively with individuals with different nationalities, background and competencies
• analyse the tasks and define the problems to be solved
• apply relevant project management models
• apply each group member’s existing disciplinary knowledge in innovation processes.
• effectively analyse challenges; and identify and acquire competences necessary to develop feasible solutions
• communicate in a multi-disciplinary team
• appropriately and correctly document their project work
• critically evaluate ongoing work and adjust development processes to achieve desired results

General Competence

Upon successful completion of the course, the student can:

• plan, research and execute a project as part of a team
• document the project; results, processes and competence development in a collaborative report
• present and discuss the results, processes, complexities and decisions in the project

The course consists of three weeks consecutive work that includes the following teaching methods: self-study including exercises/questionaries related to background theory and set up labtop for the course (one week). Lectures and seminars, and practical exercises in the use of different software programmes for analysis of HTS data (two week). The outcomes of the practical exercises in last week are discussed in plenary sessions. The practical exercises are obligatory.

Professor Evi Zouganeli