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Jasmijn van Heijst, persoonlijk assistent van Prof. Dr. Mai Chin A Paw
j.a.vanheijst@amsterdamumc.nl
Vacatures
Intersectionality and Stakeholder Engagement in Advanced Behavioural Data Analysis (36 hrs/week)
PhD student | Marie Skłodowska-Curie Doctoral Network Fellowship
React until: 1 April 2023
The project
LABDA (Learning Network for Advanced Behavioural Data Analysis) is an EU-funded MSCA Doctoral Network, that brings together leading researchers in advanced movement behaviour data analysis at the intersection of data science, method development, epidemiology, public health, and wearable technology to train a new generation of creative and innovative public health researchers via training-through-research. The main aims of LABDA are to establish novel methods for advanced 24/7 movement behaviour data analysis of sensor-based data, examine the added value of advanced behavioural data analysis and multi-modal data for predicting health risk and facilitate the use and interpretability of the advanced methods for application in science, policy and society. Via training-through-research projects, 13 doctoral fellows will contribute to reaching these aims. Together, they will develop a joint taxonomy to enable interoperability and data harmonisation. Results will be combined in an open-source LABDA toolbox of advanced analysis methods, including a decision tree to guide researchers and other users to the optimal method for their (research) question. The open-source toolbox of advanced analysis methods will lead to optimised, tailored public health recommendations and improved personal wearable feedback concerning 24/7 movement behaviour. For more information, see the project’s website: LABDA project
About your role
As a PhD student in the project ‘Intersectionality and Stakeholder engagement to improve the impact of Advanced Data Analysis for assessing 24/7 movement behaviours’ your challenge is to examine the applicability of various advanced analyses methods across various subgroups at the intersection of characteristics such as age, gender, ethnicity, and socio-economic position. For example, are algoritms developed on accelerometer data of highly educated white men also applicable to accelerometer data of adolescents with a migrant background attending secondary school? A second challenge is to translate the results of these intersectionality analyses into input for the optimal design of the LABDA toolbox.
Your tasks
Your specific responsibilities will be to:
- Apply intersectionality analyses to expand the impact of advanced behavioural data analyses methods;
- Engage with multiple stakeholders across public health, industry, policy, research and society regarding their needs and preferences for the development of the LABDA toolbox;
- Report on findings by publishing scientific articles, resulting in a dissertation;
- Present findings at (inter)national meetings/conferences;
- Collaborate and exchange knowledge and skills with the other LABDA fellows;
- Contribute to the LABDA toolbox of advanced analysis methods for sensor-based behavioural data;
- Contribute to educational activities of the department and within the consortium
Translation of advanced movement behaviour data to real-life behaviour and public health recommendations (36 hrs/week)
PhD student | Marie Skłodowska-Curie Doctoral Network Fellowship
React until: 1 April 2023
The project
LABDA (Learning Network for Advanced Behavioural Data Analysis) is an EU-funded MSCA Doctoral Network, that brings together leading researchers in advanced movement behaviour data analysis at the intersection of data science, method development, epidemiology, public health, and wearable technology to train a new generation of creative and innovative public health researchers via training-through-research. The main aims of LABDA are to establish novel methods for advanced 24/7 movement behaviour data analysis of sensor-based data, examine the added value of advanced behavioural data analysis and multi-modal data for predicting health risk and facilitate the use and interpretability of the advanced methods for application in science, policy and society. Via training-through-research projects, 13 doctoral fellows will contribute to reaching these aims. Together, they will develop a joint taxonomy to enable interoperability and data harmonisation. Results will be combined in an open-source LABDA toolbox of advanced analysis methods, including a decision tree to guide researchers and other users to the optimal method for their (research) question. The open-source toolbox of advanced analysis methods will lead to optimised, tailored public health recommendations and improved personal wearable feedback concerning 24/7 movement behaviour. For more information, see the project’s website: LABDA project
About your role
As a PhD student in the project ‘Translation of advanced 24/7 movement behaviour data to real-life behaviour and public health recommendations’ your challenge is to characterise behaviour profiles across subgroups using advanced methods with an intersectional approach, and to translate optimal behaviour profiles into public health recommendations. Your work will result in the contextual description of behavioural profiles of different subgroups, taking into account various characteristics including age, gender, ethnicity, and socio-economic position. In addition, you will develop an advice for developing public health recommendations based on advanced methods.
Your tasks
Your specific responsibilities will be to:
- Design and apply a mixed-methods approach to the project;
- Characterise behavioural profiles using existing cohort data based on advanced behavioural data analysis tools;
- Apply an intersectionaly lens throughout all phases of the PhD project;
- Engage with all stakeholders relevant to the translation of 24/7 movement behaviour throughout the PhD project;
- Report on findings by publishing scientific articles, resulting in a dissertation;
- Present findings at (inter)national meetings/conferences;
- Collaborate and exchange knowledge and skills with the other LABDA fellows;
- Contribute to the LABDA toolbox of advanced analysis methods for sensor-based behavioural data;
- Contribute to educational activities of the department and within the consortium.
Advancing temporal 24/7 movement behaviour data analysis (36 hrs/week)
PhD student | Marie Skłodowska-Curie Doctoral Network Fellowship
React until: 1 April 2023
The project
LABDA (Learning Network for Advanced Behavioural Data Analysis) is an EU-funded MSCA Doctoral Network, that brings together leading researchers in advanced movement behaviour data analysis at the intersection of data science, method development, epidemiology, public health, and wearable technology to train a new generation of creative and innovative public health researchers via training-through-research. The main aims of LABDA are to establish novel methods for advanced 24/7 movement behaviour data analysis of sensor-based data, examine the added value of advanced behavioural data analysis and multi-modal data for predicting health risk and facilitate the use and interpretability of the advanced methods for application in science, policy and society. Via training-through-research projects, 13 doctoral fellows will contribute to reaching these aims. Together, they will develop a joint taxonomy to enable interoperability and data harmonisation. Results will be combined in an open-source LABDA toolbox of advanced analysis methods, including a decision tree to guide researchers and other users to the optimal method for their (research) question. The open-source toolbox of advanced analysis methods will lead to optimised, tailored public health recommendations and improved personal wearable feedback concerning 24/7 movement behaviour. For more information, see the project’s website: LABDA project
About your role
As a PhD student in the project ‘Temporal data analysis of 24/7 human movement behaviour and value for health’ your challenge is to develop methods for temporal analysis of human behaviour amongst others compare temporal analysis techniques in their ability to detect change points in behaviour; explore data-driven techniques to identify subgroups with similar temporal behaviour patterns; and assess complementary, discriminatory and predictive value of temporal data analysis techniques beyond volume-based analyses.
Your tasks
Your specific responsibilities will be to:
- Design methods for temporal data analysis of 24-hour movement behaviour;
- Generate and test predictive modelling programs;
- Report on findings by publishing scientific articles, resulting in a dissertation;
- Present findings at (inter)national meetings/conferences;
- Contribute to the LABDA toolbox of advanced analysis methods for sensor-based behavioural data;
- Contribute to educational activities of the department and within the consortium.