Better understanding causes of the obesity epidemic is key to reduce obesity among children. This 3-year PhD will apply machine learning and mechanistic models to three US and Danish cohorts of 50000+ children, to systematically study the complex interaction between multiple determinants (chemical, exercise, dietary…) and obesity.

A 3-year PhD Student position is available for the dedicated researcher who wants to pursue a front runner research career in Machine Learning Assessment of Causes of the Obesity Epidemic among children, adolescents and young adults in Denmark and in the US. The position is offered at the Section for Quantitative Sustainability Assessment (QSA), Department of Environmental and Resource Engineering (DTU Sustain). QSA spearheads the development of methods and application of life cycle impact assessment and sustainability assessment of products and lifestyles, determining impacts on both human health and the environment.

DTU Sustain is one of the largest university departments specializing in environmental and resource engineering in Europe, with 300 employees from more than 30 nationalities. It develops new, more environmentally friendly technologies and new quantitative sustainability assessment methods and disseminate this knowledge to society and future generations of engineers. DTU is the most publishing and cited university worldwide in the field of Life Cycle Impact Assessment.

Why has obesity in children increased, and in particular at definite periods? This is still not well understood and is key to design more efficient interventions. To address this question, an interdisciplinary team from the Technical University Denmark, Aarhus University and University of Copenhagen launches in collaboration with US scientists a three years exploratory research granted by the Novo Nordisk Foundation: “Identification and evaluation of determinants of the obesity epidemic among children, adolescents and young adults in Denmark and in the US”.

Within this project, the present PhD project will aim to extend the realm of putative determinants of obesity, by applying machine learning tools and statistical analyses to a cohort of 55,000 children and young adults of the US National Health and Nutrition Examination Survey. The PhD will also use mechanistic models of obesity to analyse deviations from normal weight trends. Work will be carried out in collaboration with partners, who will focus on the other two Danish cohorts and on historic analysis.

Responsibilities and qualifications
The successful candidate will develop and apply machine learning techniques to systematically study the relationship between determinants and obesity, analyzing the complex interactions between traditional (diet, exercise) and less conventional potential determinants such as chemicals or pharmaceuticals. Compared to traditional epidemiological approaches, exploratory analyses have the strength of letting the “data speak”, enabling to formulate new data-based hypotheses.

The main tasks include:

• Complement the already available curated data of the US NHANES Survey of 55,000 children participants, with additional data on birth and life events.
• Apply Machine learning techniques, starting with Random Survival Forest to explore the combined influences on obesity of multiple determinants including foods, physical activities, physiological indicators, demographics, and exposures to chemicals and pharmaceuticals.
• Develop Shapley type visualization and interpretation tools for complex causal structures, to apprehend the multiple interactions between exposure factors.
• Use existing mechanistic models of obesity to predict trajectories of body weight and fat mass, in order to compare these with observed body mass and analyze substantial deviations.
• Use these models in a backward application, starting from observed BMI and fat mass, to analyze deviations between predicted and observed energy expenditures to identify deviations and their correlations with putative associations.
• Compare and contrast results from the NHANES cohorts with the Danish cohorts.
• Provide insights on key determinants of the obesity epidemics.

Additionally, the candidate is expected to participate in the department’s teaching and dissemination activities and to the supervision of BSc/MSc thesis. The candidate will also have the opportunity to collaborate with Prof. Thorkild I.A. Sørensen (KU) and Prof. Cecilia Ramlau-Hansen and Christina Dahm (.Aarhus University).

You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree. The degree should preferably be within Data science, Bioinformatics, Mathematical Modelling and Computation, Environmental Engineering, Epidemiology, Nutrition epidemiology or equivalent. Knowledge of machine learning techniques, data management, and data analysis as well as written and spoken English, are required.

Approval and Enrolment
The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education.

Assessment
The assessment of the applicants will be made by Prof. Olivier Jolliet. One to two rounds of formal interviews are expected. Further, an HR personality test and an English skills test (if needed) are also part of the recruitment procedure at DTU Sustain.

We offer
DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility.

Salary and appointment terms
The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. The allowance will be agreed upon with the relevant union. The period of employment is 3 years.

The position is a full-time position. Expected PhD start is 1 August 2023. The workplace will be at DTU Lyngby Campus.

You can read more about career paths at DTU here.

Further information
Further information may be obtained from Olivier Jolliet (ojoll@dtu.dk).

You can read more about the department at www.sustain.dtu.dk

If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark. Furthermore, you have the option of joining our monthly free seminar “PhD relocation to Denmark and startup “Zoom” seminar” for all questions regarding the practical matters of moving to Denmark and working as a PhD at DTU.

Application procedure
Your complete online application must be submitted no later than 2 May 2023 (23:59 Danish time).

Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link "Apply now", fill out the online application form, and attach all your materials in English in one PDF file. The file must include:

• A letter motivating the application (cover letter)
• Curriculum vitae
• Grade transcripts and BSc/MSc diploma (in English) including official description of grading scale
• Documentation of knowledge (evidence of experience, project, course, etc.) in one or more of the following fields: data science, programming, bioinformatics, epidemiology, nutritional epidemiology, environmental health

You may apply prior to ob­tai­ning your master's degree but cannot begin before having received it.

Applications received after the deadline will not be considered.

All interested candidates irrespective of age, gender, race, disability, religion or ethnic background are encouraged to apply.

DTU Sustain - Department of Environmental and Resource Engineering - is one of the largest university departments specializing in environmental and resource engineering in Europe. The department conducts research, development & scientific advice and provides educational programs and service to society.  We are working to develop new environmentally friendly and sustainable technologies, methods and solutions, and to disseminate this knowledge to society and future generations of engineers. The Department has approximately 300 staff from more than 30 nationalities.

Technology for people
DTU develops technology for people. With our international elite research and study programmes, we are helping to create a better world and to solve the global challenges formulated in the UN’s 17 Sustainable Development Goals. Hans Christian Ørsted founded DTU in 1829 with a clear mission to develop and create value using science and engineering to benefit society. That mission lives on today. DTU has 13,500 students and 6,000 employees. We work in an international atmosphere and have an inclusive, evolving, and informal working environment. DTU has campuses in all parts of Denmark and in Greenland, and we collaborate with the best universities around the world.