PhD Position on Advanced Network Approaches to Enhance Youth Mental Health

1 Mar 2024
Job Information

Organisation/Company

Utrecht University

Research Field

Cultural studies

Researcher Profile

First Stage Researcher (R1)

Country

Netherlands

Application Deadline

15 Apr 2024 - 21:59 (UTC)

Type of Contract

Temporary

Job Status

Not Applicable

Hours Per Week

40.0

Is the job funded through the EU Research Framework Programme?

Not funded by an EU programme

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

Ready to make a real difference? Are you interested in applying cutting-edge statistical and machine learning techniques to develop and apply innovative network models uncovering psychopathology in adolescence? Join our interdisciplinary team! We're seeking a highly motivated and interested PhD candidate who aspires to make meaningful impact in the field. Apply now for this exciting PhD project and be part of the future.

Your job
The department of Methodology and Statistics has a job opening for a PhD candidate interested in applying advanced statistical and machine learning techniques to develop and apply advanced network models applicable in Youth Mental Health Research, aiming to improve youth mental health. This interdisciplinary project is a close collaboration between the department of Methodology and Statistics and the department of Interdisciplinary Social Science .

Adolescence is a phase characterised by heightened susceptibility to mental health problems. Globally, adolescents experience mental health problems, mostly depression or anxiety. Most mental disorders develop during adolescence and have negative long-term consequences. Despite their high prevalence, the progress in understanding and treating mental disorders has been limited. Many mental disorders lack effective treatment options, and sustained recovery is achieved by only a portion of the patients. Furthermore, Comorbidity is widespread among adolescents. The number of adolescents with comorbidity is higher than those with single disorders. However, despite extensive research, identifying underlying causes for mental disorders has been largely unsuccessful.

In this project, you will develop and utilise advanced network approaches to investigate youth mental health. The network approach can be understood as a theoretical framework to explain the existence and development of mental disorders. The essence of the network approach is that the system of interacting symptoms constitutes the disorder, meaning that the causes of the disorder lie in the symptoms and their interactions. The network framework is able to provide a new perspective on why mental disorders co-occur, implying that comorbidity is an inherent feature of mental disorders.

The aim of this project is to uncover dynamic symptom development investigate the interdependence between disorders, symptoms and comorbidity, and inform new targeted interventions based on insights from these developments. You will implement the developed innovative algorithm in this project in a statistical package within the open statistical software environment. To validate the developed model, you will first conduct an extensive simulation study on the estimation and prediction performance of the developed algorithm then you apply the model on the real data. Specifically, you will use a longitudinal cohort study among adolescents with broadly oriented data with information on social, psychological and physical factors. The data set include variables measuring depression, anxiety, internalising and externalising problems, eating disorders, antisocial behaviour, psychotic symptoms, and several psychiatric diagnoses.

You will work in close collaboration with the supervisory team, and you will be supported and supervised by academic staff from both departments. The responsibilities will encompass:

• developing and applying advanced Network Models using advance statistical and machine learning techniques;
• developing open-access software tools (such as R and Python packages) for applying the newly developed algorithms/models and techniques to real-world datasets;
• writing up findings for publication;
• presenting research findings at leading conferences;
• attending classes and seminars (including those offered at other universities) to further develop thinking and research skills;
• conducting teaching (between 10% and 20%), including undergraduate tutorials.

Requirements
Specific Requirements

We are looking for an enthusiastic colleague who meets the requirements below:

• holds (or nearly holds) a Master’s degree in Methodology and Statistics, data science, Econometrics, Psychometrics, (Clinical) Psychology/Pedagogy or a related field;
• strong programming skills for example in R, or Python, or Matlab, or C/C++;
• affinity (and preferably experience) with intense longitudinal data and/or Network modelling and network analysis;
• excellent verbal and written communication skills in English;
• a motivated and collaborative team member, who is communicative and open for collaboration across scientific fields;
• is able to meet deadlines and conduct research independently as well as being part of a team.

Additional Information
Benefits

We offer:

• a position for 4-4,5 years (depending on amount of teaching) with a trial period of one year;
• a full-time gross monthly salary between € 2.770,- in the first year and € 3.539,- in the fourth year of employment (salary scale P under the Collective Labour Agreement for Dutch Universities (CAO NU);
• 8% holiday pay and 8.3% year-end bonus;
• a pension scheme, partially paid parental leave and flexible terms of employment based on the CAO NU.

In addition to the terms of employment laid down in the CAO NU, Utrecht University has a number of schemes and facilities of its own for employees. This includes schemes facilitating professional development , leave schemes and schemes for sports and cultural activities , as well as discounts on software and other IT products. We also offer access to additional employee benefits through our Terms of Employment Options Model. In this way, we encourage our employees to continue to invest in their growth. For more information, please visit Working at Utrecht University .

Selection process

As Utrecht University, we want to be a home for everyone. We value staff with diverse backgrounds, perspectives and identities, including cultural, religious or ethnic background, gender, sexual orientation, disability or age. We strive to create a safe and inclusive environment in which everyone can flourish and contribute.

To apply, please send your curriculum vitae, including a letter of motivation, via the ‘apply now’ button.

The first round of interviews takes place on 24 April 2024. An assessment is part of the procedure. Our preferred start date is September/October 2024.

Additional comments

For more information, please contact Kevin van Kats at [email protected] .

Please note that international candidates that need a visa/work permit for the Netherlands require at least four months processing time after selection and acceptance. Our International Service Desk (ISD) can answer your questions about living in the Netherlands as international staff . Finding appropriate housing in or near Utrecht is your own responsibility, but the ISD may be able to advise you therewith. In case of general questions about working and living in The Netherlands, please consult the Dutch Mobility Portal .

Candidates for this vacancy will be recruited by Utrecht University.

Website for additional job details

https://www.academictransfer.com/338403/

Work Location(s)

Number of offers available

1

Company/Institute

Universiteit Utrecht

Country

Netherlands

City

Utrecht

Postal Code

3584CH

Street

Padualaan 14

Geofield

Where to apply

Website

https://www.academictransfer.com/en/338403/phd-position-on-advanced-network-app…

Contact

City

Utrecht

Website

http://www.uu.nl/

Street

Domplein 29

Postal Code

3512 JE

STATUS: EXPIRED