PhD student in Computer and Systems Sciences with focus on Data Science Ref. No. SU FV-0853-17 at the Department of Computer and Systems Sciences. Closing date: 15 April 2017.
The Department of Computer and Systems Sciences (DSV) is one of the oldest IT departments in Sweden and the largest department at Stockholm University with 6,000 students. The field of computer and systems sciences bridges the gap between technology, humanities, social sciences, and behavioral sciences. The use and design of IT is put into context and in relation to people, organizations, and society. DSV offers a stimulating research environment and a strong research group in the area of data science with well-established national and international connections to leading industrial and academic institutions.
Project description
The main goal of the thesis project is to develop a generic framework that will advance the current state-of-the-art in the areas of temporal Data Mining and Healthcare Informatics, and will develop novel methods and tools for searching and mining massive and heterogeneous data sources of temporal nature with an emphasis on the application area of healthcare. The methods will be evaluated on large real datasets from the healthcare domain, a wide variety of public datasets, as well as synthetic data. The focus application area will be the detection of adverse events in healthcare, such as adverse drug events, while secondary focus will be on understanding and modelling heart failure patients. The thesis project will be closely linked to the Starting Grant Project titled "Temporal Data Mining for Detecting Adverse Events in Healthcare", funded by the Swedish Research Council between 2017 and 2020. More information about this project can be found at: papapetrou.blogs.dsv.su.se/projects.
Qualification requirements
In order to meet the general entry requirements, the applicant must have completed a second-cycle degree, completed courses equivalent to at least 240 higher education credits, of which 60 credits must be in the second cycle, or have otherwise acquired equivalent knowledge in Sweden or elsewhere. In order to meet the specific entry requirements, the applicant must have completed courses at second-cycle level in computer and systems sciences with a minimum of 90 higher education credits, or equivalent, including a thesis with a minimum of 15 higher education credits. In addition, the applicant is required to have English language skills equivalent to Level B2 as defined by the Common European Framework of Reference for Languages. Both general and specific academic entry requirements must be met. Only a person who will be or has already been admitted to a third-cycle programme may be appointed to a doctoral studentship. The primary assessment criteria in appointing a doctoral student should be the capacity to benefit from the training. Selection The selection among the eligible candidates will be based on their capacity to benefit from the training.
The following criteria will be used to assess this capacity:
* Knowledge and experience in at least one of the following areas: data mining, machine learning, databases, statistical methods, temporal data mining, healthcare analytics
* Experience in the design, analysis, development, implementation, and evaluation of algorithms for data mining * Excellent programming skills in at least one of the following programming languages or tools: C, C++, Python, Java, Perl, Matlab, R
* Ability to formulate, motivate, and address research problems and research questions
* Ability to communicate and cooperate well with colleagues and researchers
* Ability to manage own projects, including the ability to adhere to deadlines
* Ability to develop and lead activities in collaboration with stakeholders and domain experts.
Admission Regulations for Doctoral Studies at Stockholm University are available at: www.su.se/rules.
Terms of employment
The term of the initial contract may not exceed one year. The employment may be extended for a maximum of two years at a time. However, the total period of employment may not exceed the equivalent of four years of full-time study. Doctoral students should primarily devote themselves to their own education, but may engage in teaching, research, and administration corresponding to a maximum of 20 % of a full-time position. Please note that admission decisions cannot be appealed. Stockholm University strives to be a workplace free from discrimination and with equal opportunities for all.
Contact For more information, please contact Associate Professor Panagiotis Papapetrou, [email protected], or the Director of PhD studies, Sirkku M?nnikk?-Barbutiu, [email protected]. Union representatives Anqi Lindblom-Ahlm (Saco-S) and Lisbeth H?ggberg (Fackf?rbundet ST and L?rarf?rbundet), telephone: +46 8 16 20 00 (operator), [email protected] (SEKO), and PhD student representative, [email protected].
Application
Apply for the position at Stockholm University's recruitment system by clicking the "Apply" button. It is the responsibility of the applicant to ensure that the application is complete in accordance with the instructions in the job advertisement, and that it is submitted before the deadline. Please include the following information with your application
* Your contact details and personal data
* Your highest degree
* Your language skills
* Contact details for 2-3 references and, in addition, please include the following documents
* Cover letter
* CV - degrees and other completed courses, work experience and a list of degree projects/theses
* Research proposal (no more than 3 pages) describing: - why you are interested in the field/project described in the advertisement - why and how you wish to complete the project - what makes you suitable for the project in question
* Degree certificates and grades confirming that you meet the general and specific entry requirements (no more than 6 files)
* Letters of recommendation (no more than 3 files)
* Degree projects/theses (no more than 3 files). The instructions for applicants are available at: Instructions - Applicants. You are welcome to apply! Stockholm University - our education and research produce results.
The Department of Computer and Systems Sciences (DSV) is one of the oldest IT departments in Sweden and the largest department at Stockholm University with 6,000 students. The field of computer and systems sciences bridges the gap between technology, humanities, social sciences, and behavioral sciences. The use and design of IT is put into context and in relation to people, organizations, and society. DSV offers a stimulating research environment and a strong research group in the area of data science with well-established national and international connections to leading industrial and academic institutions.
Project description
The main goal of the thesis project is to develop a generic framework that will advance the current state-of-the-art in the areas of temporal Data Mining and Healthcare Informatics, and will develop novel methods and tools for searching and mining massive and heterogeneous data sources of temporal nature with an emphasis on the application area of healthcare. The methods will be evaluated on large real datasets from the healthcare domain, a wide variety of public datasets, as well as synthetic data. The focus application area will be the detection of adverse events in healthcare, such as adverse drug events, while secondary focus will be on understanding and modelling heart failure patients. The thesis project will be closely linked to the Starting Grant Project titled "Temporal Data Mining for Detecting Adverse Events in Healthcare", funded by the Swedish Research Council between 2017 and 2020. More information about this project can be found at: papapetrou.blogs.dsv.su.se/projects.
Qualification requirements
In order to meet the general entry requirements, the applicant must have completed a second-cycle degree, completed courses equivalent to at least 240 higher education credits, of which 60 credits must be in the second cycle, or have otherwise acquired equivalent knowledge in Sweden or elsewhere. In order to meet the specific entry requirements, the applicant must have completed courses at second-cycle level in computer and systems sciences with a minimum of 90 higher education credits, or equivalent, including a thesis with a minimum of 15 higher education credits. In addition, the applicant is required to have English language skills equivalent to Level B2 as defined by the Common European Framework of Reference for Languages. Both general and specific academic entry requirements must be met. Only a person who will be or has already been admitted to a third-cycle programme may be appointed to a doctoral studentship. The primary assessment criteria in appointing a doctoral student should be the capacity to benefit from the training. Selection The selection among the eligible candidates will be based on their capacity to benefit from the training.
The following criteria will be used to assess this capacity:
* Knowledge and experience in at least one of the following areas: data mining, machine learning, databases, statistical methods, temporal data mining, healthcare analytics
* Experience in the design, analysis, development, implementation, and evaluation of algorithms for data mining * Excellent programming skills in at least one of the following programming languages or tools: C, C++, Python, Java, Perl, Matlab, R
* Ability to formulate, motivate, and address research problems and research questions
* Ability to communicate and cooperate well with colleagues and researchers
* Ability to manage own projects, including the ability to adhere to deadlines
* Ability to develop and lead activities in collaboration with stakeholders and domain experts.
Admission Regulations for Doctoral Studies at Stockholm University are available at: www.su.se/rules.
Terms of employment
The term of the initial contract may not exceed one year. The employment may be extended for a maximum of two years at a time. However, the total period of employment may not exceed the equivalent of four years of full-time study. Doctoral students should primarily devote themselves to their own education, but may engage in teaching, research, and administration corresponding to a maximum of 20 % of a full-time position. Please note that admission decisions cannot be appealed. Stockholm University strives to be a workplace free from discrimination and with equal opportunities for all.
Contact For more information, please contact Associate Professor Panagiotis Papapetrou, [email protected], or the Director of PhD studies, Sirkku M?nnikk?-Barbutiu, [email protected]. Union representatives Anqi Lindblom-Ahlm (Saco-S) and Lisbeth H?ggberg (Fackf?rbundet ST and L?rarf?rbundet), telephone: +46 8 16 20 00 (operator), [email protected] (SEKO), and PhD student representative, [email protected].
Application
Apply for the position at Stockholm University's recruitment system by clicking the "Apply" button. It is the responsibility of the applicant to ensure that the application is complete in accordance with the instructions in the job advertisement, and that it is submitted before the deadline. Please include the following information with your application
* Your contact details and personal data
* Your highest degree
* Your language skills
* Contact details for 2-3 references and, in addition, please include the following documents
* Cover letter
* CV - degrees and other completed courses, work experience and a list of degree projects/theses
* Research proposal (no more than 3 pages) describing: - why you are interested in the field/project described in the advertisement - why and how you wish to complete the project - what makes you suitable for the project in question
* Degree certificates and grades confirming that you meet the general and specific entry requirements (no more than 6 files)
* Letters of recommendation (no more than 3 files)
* Degree projects/theses (no more than 3 files). The instructions for applicants are available at: Instructions - Applicants. You are welcome to apply! Stockholm University - our education and research produce results.
